PyCaret_OD_rio_Piracicaba.ipynb

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        "testando previsao OD rio Piracicaba"
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            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pycaret\n",
            "  Downloading pycaret-3.0.2-py3-none-any.whl (483 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m483.6/483.6 kB\u001b[0m \u001b[31m30.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: ipython>=5.5.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.34.0)\n",
            "Requirement already satisfied: ipywidgets>=7.6.5 in /usr/local/lib/python3.10/dist-packages (from pycaret) (7.7.1)\n",
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            "Requirement already satisfied: numpy<1.24,>=1.21 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.22.4)\n",
            "Requirement already satisfied: pandas<2.0.0,>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.5.3)\n",
            "Requirement already satisfied: jinja2>=1.2 in /usr/local/lib/python3.10/dist-packages (from pycaret) (3.1.2)\n",
            "Requirement already satisfied: scipy<2.0.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.10.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.2.0)\n",
            "Requirement already satisfied: scikit-learn>=1.0 in /usr/local/lib/python3.10/dist-packages (from pycaret) (1.2.2)\n",
            "Collecting pyod>=1.0.8 (from pycaret)\n",
            "  Downloading pyod-1.0.9.tar.gz (149 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m150.0/150.0 kB\u001b[0m \u001b[31m18.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: imbalanced-learn>=0.8.1 in /usr/local/lib/python3.10/dist-packages (from pycaret) (0.10.1)\n",
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            "Requirement already satisfied: Send2Trash>=1.8.0 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.8.0)\n",
            "Requirement already satisfied: terminado>=0.8.3 in /usr/local/lib/python3.10/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.17.1)\n",
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            "Requirement already satisfied: argon2-cffi-bindings in /usr/local/lib/python3.10/dist-packages (from argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (21.2.0)\n",
            "Requirement already satisfied: lxml in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.9.2)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (4.11.2)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (6.0.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.7.1)\n",
            "Requirement already satisfied: entrypoints>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.4)\n",
            "Requirement already satisfied: jupyterlab-pygments in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.2.2)\n",
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            "Requirement already satisfied: nbclient>=0.5.0 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.7.4)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.5.0)\n",
            "Requirement already satisfied: tinycss2 in /usr/local/lib/python3.10/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.2.1)\n",
            "Requirement already satisfied: cffi>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (1.15.1)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.4.1)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (0.5.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.10/dist-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.6.5->pycaret) (2.21)\n",
            "Building wheels for collected packages: plotly-resampler, pyod\n",
            "  Building wheel for plotly-resampler (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for plotly-resampler: filename=plotly_resampler-0.8.3.2-cp310-cp310-manylinux_2_31_x86_64.whl size=75235 sha256=f83193b47a25b017438d0c26e96b6d489540a434218977551d2d582150336706\n",
            "  Stored in directory: /root/.cache/pip/wheels/a9/19/e2/ec856d98cf9b617511fff0abae1dfed5bf1f6093ce65126b45\n",
            "  Building wheel for pyod (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyod: filename=pyod-1.0.9-py3-none-any.whl size=184097 sha256=c55e083968c3660334a747eed421bd01328c6185a76d3ed0c53c7bf99c967303\n",
            "  Stored in directory: /root/.cache/pip/wheels/83/55/6b/552e083cf5509c0afe808b76cf434f1be284d01a112623bd37\n",
            "Successfully built plotly-resampler pyod\n",
            "Installing collected packages: trace-updater, kaleido, dash-table, dash-html-components, dash-core-components, xxhash, wurlitzer, Werkzeug, schemdraw, retrying, orjson, jedi, importlib-metadata, deprecation, deprecated, ansi2html, sktime, scikit-plot, pyod, dash, pmdarima, jupyter-dash, category-encoders, tbats, plotly-resampler, pycaret\n",
            "  Attempting uninstall: Werkzeug\n",
            "    Found existing installation: Werkzeug 2.3.0\n",
            "    Uninstalling Werkzeug-2.3.0:\n",
            "      Successfully uninstalled Werkzeug-2.3.0\n",
            "Successfully installed Werkzeug-2.2.3 ansi2html-1.8.0 category-encoders-2.6.1 dash-2.10.2 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 deprecated-1.2.14 deprecation-2.1.0 importlib-metadata-6.6.0 jedi-0.18.2 jupyter-dash-0.4.2 kaleido-0.2.1 orjson-3.9.1 plotly-resampler-0.8.3.2 pmdarima-2.0.3 pycaret-3.0.2 pyod-1.0.9 retrying-1.3.4 schemdraw-0.15 scikit-plot-0.3.7 sktime-0.17.0 tbats-1.1.3 trace-updater-0.0.9.1 wurlitzer-3.0.3 xxhash-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "DwyNoETCTjt_",
        "outputId": "7d2faef3-1716-4a29-d027-e361fa120e7d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ecf596b8-bf32-4ad8-9c97-d65c7fbac9b4\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ecf596b8-bf32-4ad8-9c97-d65c7fbac9b4\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving rio_Piracicaba.csv to rio_Piracicaba.csv\n"
          ]
        }
      ],
      "source": [
        "# carregar matriz geral de dados\n",
        "import io\n",
        "import pandas as pd\n",
        "\n",
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "data = pd.read_csv((io.BytesIO(uploaded['rio_Piracicaba.csv'])), sep=\";\", decimal=\",\", header=0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "CLZLv8XVU3j3",
        "outputId": "54582154-9044-470d-e0ef-c0a1e75c73ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Temp   pH   Turb   Fosf   NH3   Kje   NO3   NO2  DBO  Vazao   ppt   OD\n",
              "0    27.0  7.2  42.00  0.151  0.22  0.93  0.37  0.09    6  306.0   0.0  6.2\n",
              "1    25.0  7.2  45.00  0.140  0.26  1.08  0.36  0.13    3  175.0   0.0  5.4\n",
              "2    17.0  7.0  13.00  0.183  0.12  0.99  0.71  0.09    3   92.0   0.0  6.0\n",
              "3    18.0  6.9  17.00  0.107  0.22  1.04  0.51  0.13    6   85.0   1.0  4.0\n",
              "4    25.0  6.9  10.00  0.213  1.00  2.04  0.44  0.17    6   68.0  12.6  2.6\n",
              "..    ...  ...    ...    ...   ...   ...   ...   ...  ...    ...   ...  ...\n",
              "143  25.0  7.1  49.50  0.007  0.50  0.80  1.34  0.30    7  136.0   0.7  5.5\n",
              "144  22.3  7.2  15.70  0.062  1.00  1.00  1.41  0.22    7  147.0   0.0  5.8\n",
              "145  18.3  7.1  16.20  0.165  1.00  1.00  1.40  0.20    7  121.0  13.2  6.9\n",
              "146  23.6  7.1   8.95  0.338  2.00  3.00  1.16  0.22    9   57.0   0.0  4.8\n",
              "147  27.3  7.0  48.80  0.392  2.00  3.00  1.16  0.31    9   65.0   3.4  3.7\n",
              "\n",
              "[148 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e6f2c0a9-1a64-436c-990e-efbc03ea7df2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temp</th>\n",
              "      <th>pH</th>\n",
              "      <th>Turb</th>\n",
              "      <th>Fosf</th>\n",
              "      <th>NH3</th>\n",
              "      <th>Kje</th>\n",
              "      <th>NO3</th>\n",
              "      <th>NO2</th>\n",
              "      <th>DBO</th>\n",
              "      <th>Vazao</th>\n",
              "      <th>ppt</th>\n",
              "      <th>OD</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.0</td>\n",
              "      <td>7.2</td>\n",
              "      <td>42.00</td>\n",
              "      <td>0.151</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.09</td>\n",
              "      <td>6</td>\n",
              "      <td>306.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25.0</td>\n",
              "      <td>7.2</td>\n",
              "      <td>45.00</td>\n",
              "      <td>0.140</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.13</td>\n",
              "      <td>3</td>\n",
              "      <td>175.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>13.00</td>\n",
              "      <td>0.183</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.99</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.09</td>\n",
              "      <td>3</td>\n",
              "      <td>92.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>17.00</td>\n",
              "      <td>0.107</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.04</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.13</td>\n",
              "      <td>6</td>\n",
              "      <td>85.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>25.0</td>\n",
              "      <td>6.9</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.213</td>\n",
              "      <td>1.00</td>\n",
              "      <td>2.04</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.17</td>\n",
              "      <td>6</td>\n",
              "      <td>68.0</td>\n",
              "      <td>12.6</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>143</th>\n",
              "      <td>25.0</td>\n",
              "      <td>7.1</td>\n",
              "      <td>49.50</td>\n",
              "      <td>0.007</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.34</td>\n",
              "      <td>0.30</td>\n",
              "      <td>7</td>\n",
              "      <td>136.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>5.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>144</th>\n",
              "      <td>22.3</td>\n",
              "      <td>7.2</td>\n",
              "      <td>15.70</td>\n",
              "      <td>0.062</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.41</td>\n",
              "      <td>0.22</td>\n",
              "      <td>7</td>\n",
              "      <td>147.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>18.3</td>\n",
              "      <td>7.1</td>\n",
              "      <td>16.20</td>\n",
              "      <td>0.165</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.20</td>\n",
              "      <td>7</td>\n",
              "      <td>121.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>6.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>23.6</td>\n",
              "      <td>7.1</td>\n",
              "      <td>8.95</td>\n",
              "      <td>0.338</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.16</td>\n",
              "      <td>0.22</td>\n",
              "      <td>9</td>\n",
              "      <td>57.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>27.3</td>\n",
              "      <td>7.0</td>\n",
              "      <td>48.80</td>\n",
              "      <td>0.392</td>\n",
              "      <td>2.00</td>\n",
              "      <td>3.00</td>\n",
              "      <td>1.16</td>\n",
              "      <td>0.31</td>\n",
              "      <td>9</td>\n",
              "      <td>65.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>3.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>148 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e6f2c0a9-1a64-436c-990e-efbc03ea7df2')\"\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e6f2c0a9-1a64-436c-990e-efbc03ea7df2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9MgSowt3U3gV",
        "outputId": "9819ce43-8eef-4826-ea60-0e7403e082da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Temp', 'pH', 'Turb', 'Fosf', 'NH3', 'Kje', 'NO3', 'NO2', 'DBO',\n",
              "       'Vazao', 'ppt', 'OD'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "saida = data['OD']"
      ],
      "metadata": {
        "id": "EUWnQBr4VUoc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.loc[:, ['Temp', 'pH', 'Turb', 'Fosf', 'NH3', 'Kje', 'NO3', 'NO2', 'DBO',\n",
        "       'Vazao', 'ppt']]"
      ],
      "metadata": {
        "id": "su2qLzYVVUed"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#escalonamento da matriz X\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler(with_mean=True, with_std=True)\n",
        "X2 = pd.DataFrame(scaler.fit_transform(X))"
      ],
      "metadata": {
        "id": "wketiMtIV0mv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pycaret.regression import *\n",
        "\n",
        "modelo = setup(data = X2, target = saida, train_size = 0.86, remove_outliers=True, fold = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        },
        "id": "8uYHEGclWBIX",
        "outputId": "c8579faf-65e5-4a67-da8f-21d5d3a110f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f7a12ef7850>"
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              "</style>\n",
              "<table id=\"T_21201\" class=\"dataframe\">\n",
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              "      <th class=\"blank level0\" >&nbsp;</th>\n",
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              "      <th id=\"T_21201_level0_col1\" class=\"col_heading level0 col1\" >Value</th>\n",
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              "    <tr>\n",
              "      <th id=\"T_21201_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_21201_row0_col0\" class=\"data row0 col0\" >Session id</td>\n",
              "      <td id=\"T_21201_row0_col1\" class=\"data row0 col1\" >6477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_21201_row1_col0\" class=\"data row1 col0\" >Target</td>\n",
              "      <td id=\"T_21201_row1_col1\" class=\"data row1 col1\" >OD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_21201_row2_col0\" class=\"data row2 col0\" >Target type</td>\n",
              "      <td id=\"T_21201_row2_col1\" class=\"data row2 col1\" >Regression</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_21201_row3_col0\" class=\"data row3 col0\" >Original data shape</td>\n",
              "      <td id=\"T_21201_row3_col1\" class=\"data row3 col1\" >(148, 12)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_21201_row4_col0\" class=\"data row4 col0\" >Transformed data shape</td>\n",
              "      <td id=\"T_21201_row4_col1\" class=\"data row4 col1\" >(141, 12)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_21201_row5_col0\" class=\"data row5 col0\" >Transformed train set shape</td>\n",
              "      <td id=\"T_21201_row5_col1\" class=\"data row5 col1\" >(120, 12)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_21201_row6_col0\" class=\"data row6 col0\" >Transformed test set shape</td>\n",
              "      <td id=\"T_21201_row6_col1\" class=\"data row6 col1\" >(21, 12)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_21201_row7_col0\" class=\"data row7 col0\" >Numeric features</td>\n",
              "      <td id=\"T_21201_row7_col1\" class=\"data row7 col1\" >11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_21201_row8_col0\" class=\"data row8 col0\" >Preprocess</td>\n",
              "      <td id=\"T_21201_row8_col1\" class=\"data row8 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_21201_row9_col0\" class=\"data row9 col0\" >Imputation type</td>\n",
              "      <td id=\"T_21201_row9_col1\" class=\"data row9 col1\" >simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
              "      <td id=\"T_21201_row10_col0\" class=\"data row10 col0\" >Numeric imputation</td>\n",
              "      <td id=\"T_21201_row10_col1\" class=\"data row10 col1\" >mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
              "      <td id=\"T_21201_row11_col0\" class=\"data row11 col0\" >Categorical imputation</td>\n",
              "      <td id=\"T_21201_row11_col1\" class=\"data row11 col1\" >mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
              "      <td id=\"T_21201_row12_col0\" class=\"data row12 col0\" >Remove outliers</td>\n",
              "      <td id=\"T_21201_row12_col1\" class=\"data row12 col1\" >True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
              "      <td id=\"T_21201_row13_col0\" class=\"data row13 col0\" >Outliers threshold</td>\n",
              "      <td id=\"T_21201_row13_col1\" class=\"data row13 col1\" >0.050000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
              "      <td id=\"T_21201_row14_col0\" class=\"data row14 col0\" >Fold Generator</td>\n",
              "      <td id=\"T_21201_row14_col1\" class=\"data row14 col1\" >KFold</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
              "      <td id=\"T_21201_row15_col0\" class=\"data row15 col0\" >Fold Number</td>\n",
              "      <td id=\"T_21201_row15_col1\" class=\"data row15 col1\" >10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
              "      <td id=\"T_21201_row16_col0\" class=\"data row16 col0\" >CPU Jobs</td>\n",
              "      <td id=\"T_21201_row16_col1\" class=\"data row16 col1\" >-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
              "      <td id=\"T_21201_row17_col0\" class=\"data row17 col0\" >Use GPU</td>\n",
              "      <td id=\"T_21201_row17_col1\" class=\"data row17 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
              "      <td id=\"T_21201_row18_col0\" class=\"data row18 col0\" >Log Experiment</td>\n",
              "      <td id=\"T_21201_row18_col1\" class=\"data row18 col1\" >False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
              "      <td id=\"T_21201_row19_col0\" class=\"data row19 col0\" >Experiment Name</td>\n",
              "      <td id=\"T_21201_row19_col1\" class=\"data row19 col1\" >reg-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_21201_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
              "      <td id=\"T_21201_row20_col0\" class=\"data row20 col0\" >USI</td>\n",
              "      <td id=\"T_21201_row20_col1\" class=\"data row20 col1\" >5834</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best = compare_models()  # erros de calibração"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645,
          "referenced_widgets": [
            "c35547e5980645b081cfd6be0bebf5a9",
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            "65d68fc0b06e464e8323f1f0c35b5b62",
            "83f9631edaa54c4d8615096bd2668d08",
            "9a367bc60ae849cc8bc2790b90ff4269",
            "5b3db5506c4d4f4d93c86598971bfc22",
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            "ebe0337467d9439a85db119ddfa77e46",
            "daee668ffc704ee285a5cc6f34d65a21",
            "937d756d063f4c5185ae908c439776a4"
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        },
        "id": "r4GQemIbWVcX",
        "outputId": "cd990b3d-f3d2-4d07-cc78-ff3b9a9f9e6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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            "text/html": []
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              "<pandas.io.formats.style.Styler at 0x7f7a138abb50>"
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            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_af6e7 th {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_af6e7_row0_col0, #T_af6e7_row0_col1, #T_af6e7_row0_col2, #T_af6e7_row0_col5, #T_af6e7_row0_col6, #T_af6e7_row1_col0, #T_af6e7_row1_col3, #T_af6e7_row1_col4, #T_af6e7_row1_col5, #T_af6e7_row1_col6, #T_af6e7_row2_col0, #T_af6e7_row2_col1, #T_af6e7_row2_col2, #T_af6e7_row2_col3, #T_af6e7_row2_col4, #T_af6e7_row3_col0, #T_af6e7_row3_col1, #T_af6e7_row3_col2, #T_af6e7_row3_col3, #T_af6e7_row3_col4, #T_af6e7_row3_col5, #T_af6e7_row3_col6, #T_af6e7_row4_col0, #T_af6e7_row4_col1, #T_af6e7_row4_col2, #T_af6e7_row4_col3, #T_af6e7_row4_col4, #T_af6e7_row4_col5, #T_af6e7_row4_col6, #T_af6e7_row5_col0, #T_af6e7_row5_col1, #T_af6e7_row5_col2, #T_af6e7_row5_col3, #T_af6e7_row5_col4, #T_af6e7_row5_col5, #T_af6e7_row5_col6, #T_af6e7_row6_col0, #T_af6e7_row6_col1, #T_af6e7_row6_col2, #T_af6e7_row6_col3, #T_af6e7_row6_col4, #T_af6e7_row6_col5, #T_af6e7_row6_col6, #T_af6e7_row7_col0, #T_af6e7_row7_col1, #T_af6e7_row7_col2, #T_af6e7_row7_col3, #T_af6e7_row7_col4, #T_af6e7_row7_col5, #T_af6e7_row7_col6, #T_af6e7_row8_col0, #T_af6e7_row8_col1, #T_af6e7_row8_col2, #T_af6e7_row8_col3, #T_af6e7_row8_col4, #T_af6e7_row8_col5, #T_af6e7_row8_col6, #T_af6e7_row9_col0, #T_af6e7_row9_col1, #T_af6e7_row9_col2, #T_af6e7_row9_col3, #T_af6e7_row9_col4, #T_af6e7_row9_col5, #T_af6e7_row9_col6, #T_af6e7_row10_col0, #T_af6e7_row10_col1, #T_af6e7_row10_col2, #T_af6e7_row10_col3, #T_af6e7_row10_col4, #T_af6e7_row10_col5, #T_af6e7_row10_col6, #T_af6e7_row11_col0, #T_af6e7_row11_col1, #T_af6e7_row11_col2, #T_af6e7_row11_col3, #T_af6e7_row11_col4, #T_af6e7_row11_col5, #T_af6e7_row11_col6, #T_af6e7_row12_col0, #T_af6e7_row12_col1, #T_af6e7_row12_col2, #T_af6e7_row12_col3, #T_af6e7_row12_col4, #T_af6e7_row12_col5, #T_af6e7_row12_col6, #T_af6e7_row13_col0, #T_af6e7_row13_col1, #T_af6e7_row13_col2, #T_af6e7_row13_col3, #T_af6e7_row13_col4, #T_af6e7_row13_col5, #T_af6e7_row13_col6, #T_af6e7_row14_col0, #T_af6e7_row14_col1, #T_af6e7_row14_col2, #T_af6e7_row14_col3, #T_af6e7_row14_col4, #T_af6e7_row14_col5, #T_af6e7_row14_col6, #T_af6e7_row15_col0, #T_af6e7_row15_col1, #T_af6e7_row15_col2, #T_af6e7_row15_col3, #T_af6e7_row15_col4, #T_af6e7_row15_col5, #T_af6e7_row15_col6, #T_af6e7_row16_col0, #T_af6e7_row16_col1, #T_af6e7_row16_col2, #T_af6e7_row16_col3, #T_af6e7_row16_col4, #T_af6e7_row16_col5, #T_af6e7_row16_col6, #T_af6e7_row17_col0, #T_af6e7_row17_col1, #T_af6e7_row17_col2, #T_af6e7_row17_col3, #T_af6e7_row17_col4, #T_af6e7_row17_col5, #T_af6e7_row17_col6, #T_af6e7_row18_col0, #T_af6e7_row18_col1, #T_af6e7_row18_col2, #T_af6e7_row18_col3, #T_af6e7_row18_col4, #T_af6e7_row18_col5, #T_af6e7_row18_col6 {\n",
              "  text-align: left;\n",
              "}\n",
              "#T_af6e7_row0_col3, #T_af6e7_row0_col4, #T_af6e7_row1_col1, #T_af6e7_row1_col2, #T_af6e7_row2_col5, #T_af6e7_row2_col6 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "}\n",
              "#T_af6e7_row0_col7, #T_af6e7_row1_col7, #T_af6e7_row2_col7, #T_af6e7_row3_col7, #T_af6e7_row4_col7, #T_af6e7_row6_col7, #T_af6e7_row7_col7, #T_af6e7_row8_col7, #T_af6e7_row9_col7, #T_af6e7_row10_col7, #T_af6e7_row11_col7, #T_af6e7_row12_col7, #T_af6e7_row13_col7, #T_af6e7_row14_col7, #T_af6e7_row15_col7, #T_af6e7_row16_col7, #T_af6e7_row17_col7 {\n",
              "  text-align: left;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "#T_af6e7_row5_col7, #T_af6e7_row18_col7 {\n",
              "  text-align: left;\n",
              "  background-color: yellow;\n",
              "  background-color: lightgrey;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_af6e7\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_af6e7_level0_col0\" class=\"col_heading level0 col0\" >Model</th>\n",
              "      <th id=\"T_af6e7_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
              "      <th id=\"T_af6e7_level0_col2\" class=\"col_heading level0 col2\" >MSE</th>\n",
              "      <th id=\"T_af6e7_level0_col3\" class=\"col_heading level0 col3\" >RMSE</th>\n",
              "      <th id=\"T_af6e7_level0_col4\" class=\"col_heading level0 col4\" >R2</th>\n",
              "      <th id=\"T_af6e7_level0_col5\" class=\"col_heading level0 col5\" >RMSLE</th>\n",
              "      <th id=\"T_af6e7_level0_col6\" class=\"col_heading level0 col6\" >MAPE</th>\n",
              "      <th id=\"T_af6e7_level0_col7\" class=\"col_heading level0 col7\" >TT (Sec)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row0\" class=\"row_heading level0 row0\" >rf</th>\n",
              "      <td id=\"T_af6e7_row0_col0\" class=\"data row0 col0\" >Random Forest Regressor</td>\n",
              "      <td id=\"T_af6e7_row0_col1\" class=\"data row0 col1\" >1.0260</td>\n",
              "      <td id=\"T_af6e7_row0_col2\" class=\"data row0 col2\" >1.9566</td>\n",
              "      <td id=\"T_af6e7_row0_col3\" class=\"data row0 col3\" >1.3268</td>\n",
              "      <td id=\"T_af6e7_row0_col4\" class=\"data row0 col4\" >0.4524</td>\n",
              "      <td id=\"T_af6e7_row0_col5\" class=\"data row0 col5\" >0.3078</td>\n",
              "      <td id=\"T_af6e7_row0_col6\" class=\"data row0 col6\" >0.5450</td>\n",
              "      <td id=\"T_af6e7_row0_col7\" class=\"data row0 col7\" >0.2070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row1\" class=\"row_heading level0 row1\" >et</th>\n",
              "      <td id=\"T_af6e7_row1_col0\" class=\"data row1 col0\" >Extra Trees Regressor</td>\n",
              "      <td id=\"T_af6e7_row1_col1\" class=\"data row1 col1\" >1.0160</td>\n",
              "      <td id=\"T_af6e7_row1_col2\" class=\"data row1 col2\" >1.9412</td>\n",
              "      <td id=\"T_af6e7_row1_col3\" class=\"data row1 col3\" >1.3283</td>\n",
              "      <td id=\"T_af6e7_row1_col4\" class=\"data row1 col4\" >0.4477</td>\n",
              "      <td id=\"T_af6e7_row1_col5\" class=\"data row1 col5\" >0.3123</td>\n",
              "      <td id=\"T_af6e7_row1_col6\" class=\"data row1 col6\" >0.5453</td>\n",
              "      <td id=\"T_af6e7_row1_col7\" class=\"data row1 col7\" >0.1850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row2\" class=\"row_heading level0 row2\" >lightgbm</th>\n",
              "      <td id=\"T_af6e7_row2_col0\" class=\"data row2 col0\" >Light Gradient Boosting Machine</td>\n",
              "      <td id=\"T_af6e7_row2_col1\" class=\"data row2 col1\" >1.0238</td>\n",
              "      <td id=\"T_af6e7_row2_col2\" class=\"data row2 col2\" >1.9991</td>\n",
              "      <td id=\"T_af6e7_row2_col3\" class=\"data row2 col3\" >1.3305</td>\n",
              "      <td id=\"T_af6e7_row2_col4\" class=\"data row2 col4\" >0.4372</td>\n",
              "      <td id=\"T_af6e7_row2_col5\" class=\"data row2 col5\" >0.3006</td>\n",
              "      <td id=\"T_af6e7_row2_col6\" class=\"data row2 col6\" >0.4738</td>\n",
              "      <td id=\"T_af6e7_row2_col7\" class=\"data row2 col7\" >0.1920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row3\" class=\"row_heading level0 row3\" >ada</th>\n",
              "      <td id=\"T_af6e7_row3_col0\" class=\"data row3 col0\" >AdaBoost Regressor</td>\n",
              "      <td id=\"T_af6e7_row3_col1\" class=\"data row3 col1\" >1.0686</td>\n",
              "      <td id=\"T_af6e7_row3_col2\" class=\"data row3 col2\" >2.0665</td>\n",
              "      <td id=\"T_af6e7_row3_col3\" class=\"data row3 col3\" >1.3755</td>\n",
              "      <td id=\"T_af6e7_row3_col4\" class=\"data row3 col4\" >0.4115</td>\n",
              "      <td id=\"T_af6e7_row3_col5\" class=\"data row3 col5\" >0.3218</td>\n",
              "      <td id=\"T_af6e7_row3_col6\" class=\"data row3 col6\" >0.5549</td>\n",
              "      <td id=\"T_af6e7_row3_col7\" class=\"data row3 col7\" >0.0890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row4\" class=\"row_heading level0 row4\" >gbr</th>\n",
              "      <td id=\"T_af6e7_row4_col0\" class=\"data row4 col0\" >Gradient Boosting Regressor</td>\n",
              "      <td id=\"T_af6e7_row4_col1\" class=\"data row4 col1\" >1.0548</td>\n",
              "      <td id=\"T_af6e7_row4_col2\" class=\"data row4 col2\" >2.1299</td>\n",
              "      <td id=\"T_af6e7_row4_col3\" class=\"data row4 col3\" >1.3827</td>\n",
              "      <td id=\"T_af6e7_row4_col4\" class=\"data row4 col4\" >0.3788</td>\n",
              "      <td id=\"T_af6e7_row4_col5\" class=\"data row4 col5\" >0.3062</td>\n",
              "      <td id=\"T_af6e7_row4_col6\" class=\"data row4 col6\" >0.4959</td>\n",
              "      <td id=\"T_af6e7_row4_col7\" class=\"data row4 col7\" >0.0910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row5\" class=\"row_heading level0 row5\" >br</th>\n",
              "      <td id=\"T_af6e7_row5_col0\" class=\"data row5 col0\" >Bayesian Ridge</td>\n",
              "      <td id=\"T_af6e7_row5_col1\" class=\"data row5 col1\" >1.1668</td>\n",
              "      <td id=\"T_af6e7_row5_col2\" class=\"data row5 col2\" >2.3550</td>\n",
              "      <td id=\"T_af6e7_row5_col3\" class=\"data row5 col3\" >1.4677</td>\n",
              "      <td id=\"T_af6e7_row5_col4\" class=\"data row5 col4\" >0.2975</td>\n",
              "      <td id=\"T_af6e7_row5_col5\" class=\"data row5 col5\" >0.3321</td>\n",
              "      <td id=\"T_af6e7_row5_col6\" class=\"data row5 col6\" >0.5548</td>\n",
              "      <td id=\"T_af6e7_row5_col7\" class=\"data row5 col7\" >0.0300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row6\" class=\"row_heading level0 row6\" >knn</th>\n",
              "      <td id=\"T_af6e7_row6_col0\" class=\"data row6 col0\" >K Neighbors Regressor</td>\n",
              "      <td id=\"T_af6e7_row6_col1\" class=\"data row6 col1\" >1.2177</td>\n",
              "      <td id=\"T_af6e7_row6_col2\" class=\"data row6 col2\" >2.5500</td>\n",
              "      <td id=\"T_af6e7_row6_col3\" class=\"data row6 col3\" >1.5418</td>\n",
              "      <td id=\"T_af6e7_row6_col4\" class=\"data row6 col4\" >0.2511</td>\n",
              "      <td id=\"T_af6e7_row6_col5\" class=\"data row6 col5\" >0.3573</td>\n",
              "      <td id=\"T_af6e7_row6_col6\" class=\"data row6 col6\" >0.6363</td>\n",
              "      <td id=\"T_af6e7_row6_col7\" class=\"data row6 col7\" >0.0320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row7\" class=\"row_heading level0 row7\" >omp</th>\n",
              "      <td id=\"T_af6e7_row7_col0\" class=\"data row7 col0\" >Orthogonal Matching Pursuit</td>\n",
              "      <td id=\"T_af6e7_row7_col1\" class=\"data row7 col1\" >1.3212</td>\n",
              "      <td id=\"T_af6e7_row7_col2\" class=\"data row7 col2\" >2.8053</td>\n",
              "      <td id=\"T_af6e7_row7_col3\" class=\"data row7 col3\" >1.6290</td>\n",
              "      <td id=\"T_af6e7_row7_col4\" class=\"data row7 col4\" >0.1872</td>\n",
              "      <td id=\"T_af6e7_row7_col5\" class=\"data row7 col5\" >0.3743</td>\n",
              "      <td id=\"T_af6e7_row7_col6\" class=\"data row7 col6\" >0.7029</td>\n",
              "      <td id=\"T_af6e7_row7_col7\" class=\"data row7 col7\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row8\" class=\"row_heading level0 row8\" >huber</th>\n",
              "      <td id=\"T_af6e7_row8_col0\" class=\"data row8 col0\" >Huber Regressor</td>\n",
              "      <td id=\"T_af6e7_row8_col1\" class=\"data row8 col1\" >1.2020</td>\n",
              "      <td id=\"T_af6e7_row8_col2\" class=\"data row8 col2\" >2.6607</td>\n",
              "      <td id=\"T_af6e7_row8_col3\" class=\"data row8 col3\" >1.5465</td>\n",
              "      <td id=\"T_af6e7_row8_col4\" class=\"data row8 col4\" >0.1733</td>\n",
              "      <td id=\"T_af6e7_row8_col5\" class=\"data row8 col5\" >0.3516</td>\n",
              "      <td id=\"T_af6e7_row8_col6\" class=\"data row8 col6\" >0.5889</td>\n",
              "      <td id=\"T_af6e7_row8_col7\" class=\"data row8 col7\" >0.0330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row9\" class=\"row_heading level0 row9\" >xgboost</th>\n",
              "      <td id=\"T_af6e7_row9_col0\" class=\"data row9 col0\" >Extreme Gradient Boosting</td>\n",
              "      <td id=\"T_af6e7_row9_col1\" class=\"data row9 col1\" >1.2095</td>\n",
              "      <td id=\"T_af6e7_row9_col2\" class=\"data row9 col2\" >2.7048</td>\n",
              "      <td id=\"T_af6e7_row9_col3\" class=\"data row9 col3\" >1.5926</td>\n",
              "      <td id=\"T_af6e7_row9_col4\" class=\"data row9 col4\" >0.1694</td>\n",
              "      <td id=\"T_af6e7_row9_col5\" class=\"data row9 col5\" >0.3390</td>\n",
              "      <td id=\"T_af6e7_row9_col6\" class=\"data row9 col6\" >0.5107</td>\n",
              "      <td id=\"T_af6e7_row9_col7\" class=\"data row9 col7\" >0.1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row10\" class=\"row_heading level0 row10\" >ridge</th>\n",
              "      <td id=\"T_af6e7_row10_col0\" class=\"data row10 col0\" >Ridge Regression</td>\n",
              "      <td id=\"T_af6e7_row10_col1\" class=\"data row10 col1\" >1.2306</td>\n",
              "      <td id=\"T_af6e7_row10_col2\" class=\"data row10 col2\" >2.7345</td>\n",
              "      <td id=\"T_af6e7_row10_col3\" class=\"data row10 col3\" >1.5972</td>\n",
              "      <td id=\"T_af6e7_row10_col4\" class=\"data row10 col4\" >0.1108</td>\n",
              "      <td id=\"T_af6e7_row10_col5\" class=\"data row10 col5\" >0.3451</td>\n",
              "      <td id=\"T_af6e7_row10_col6\" class=\"data row10 col6\" >0.5729</td>\n",
              "      <td id=\"T_af6e7_row10_col7\" class=\"data row10 col7\" >0.0500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row11\" class=\"row_heading level0 row11\" >en</th>\n",
              "      <td id=\"T_af6e7_row11_col0\" class=\"data row11 col0\" >Elastic Net</td>\n",
              "      <td id=\"T_af6e7_row11_col1\" class=\"data row11 col1\" >1.4032</td>\n",
              "      <td id=\"T_af6e7_row11_col2\" class=\"data row11 col2\" >3.0303</td>\n",
              "      <td id=\"T_af6e7_row11_col3\" class=\"data row11 col3\" >1.7036</td>\n",
              "      <td id=\"T_af6e7_row11_col4\" class=\"data row11 col4\" >0.1056</td>\n",
              "      <td id=\"T_af6e7_row11_col5\" class=\"data row11 col5\" >0.3970</td>\n",
              "      <td id=\"T_af6e7_row11_col6\" class=\"data row11 col6\" >0.7680</td>\n",
              "      <td id=\"T_af6e7_row11_col7\" class=\"data row11 col7\" >0.0490</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row12\" class=\"row_heading level0 row12\" >lr</th>\n",
              "      <td id=\"T_af6e7_row12_col0\" class=\"data row12 col0\" >Linear Regression</td>\n",
              "      <td id=\"T_af6e7_row12_col1\" class=\"data row12 col1\" >1.2417</td>\n",
              "      <td id=\"T_af6e7_row12_col2\" class=\"data row12 col2\" >2.8100</td>\n",
              "      <td id=\"T_af6e7_row12_col3\" class=\"data row12 col3\" >1.6205</td>\n",
              "      <td id=\"T_af6e7_row12_col4\" class=\"data row12 col4\" >0.0745</td>\n",
              "      <td id=\"T_af6e7_row12_col5\" class=\"data row12 col5\" >0.3477</td>\n",
              "      <td id=\"T_af6e7_row12_col6\" class=\"data row12 col6\" >0.5758</td>\n",
              "      <td id=\"T_af6e7_row12_col7\" class=\"data row12 col7\" >0.4480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row13\" class=\"row_heading level0 row13\" >lar</th>\n",
              "      <td id=\"T_af6e7_row13_col0\" class=\"data row13 col0\" >Least Angle Regression</td>\n",
              "      <td id=\"T_af6e7_row13_col1\" class=\"data row13 col1\" >1.2417</td>\n",
              "      <td id=\"T_af6e7_row13_col2\" class=\"data row13 col2\" >2.8100</td>\n",
              "      <td id=\"T_af6e7_row13_col3\" class=\"data row13 col3\" >1.6205</td>\n",
              "      <td id=\"T_af6e7_row13_col4\" class=\"data row13 col4\" >0.0745</td>\n",
              "      <td id=\"T_af6e7_row13_col5\" class=\"data row13 col5\" >0.3477</td>\n",
              "      <td id=\"T_af6e7_row13_col6\" class=\"data row13 col6\" >0.5758</td>\n",
              "      <td id=\"T_af6e7_row13_col7\" class=\"data row13 col7\" >0.0580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row14\" class=\"row_heading level0 row14\" >lasso</th>\n",
              "      <td id=\"T_af6e7_row14_col0\" class=\"data row14 col0\" >Lasso Regression</td>\n",
              "      <td id=\"T_af6e7_row14_col1\" class=\"data row14 col1\" >1.5431</td>\n",
              "      <td id=\"T_af6e7_row14_col2\" class=\"data row14 col2\" >3.5423</td>\n",
              "      <td id=\"T_af6e7_row14_col3\" class=\"data row14 col3\" >1.8550</td>\n",
              "      <td id=\"T_af6e7_row14_col4\" class=\"data row14 col4\" >-0.0713</td>\n",
              "      <td id=\"T_af6e7_row14_col5\" class=\"data row14 col5\" >0.4272</td>\n",
              "      <td id=\"T_af6e7_row14_col6\" class=\"data row14 col6\" >0.8412</td>\n",
              "      <td id=\"T_af6e7_row14_col7\" class=\"data row14 col7\" >0.0450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row15\" class=\"row_heading level0 row15\" >llar</th>\n",
              "      <td id=\"T_af6e7_row15_col0\" class=\"data row15 col0\" >Lasso Least Angle Regression</td>\n",
              "      <td id=\"T_af6e7_row15_col1\" class=\"data row15 col1\" >1.5431</td>\n",
              "      <td id=\"T_af6e7_row15_col2\" class=\"data row15 col2\" >3.5423</td>\n",
              "      <td id=\"T_af6e7_row15_col3\" class=\"data row15 col3\" >1.8550</td>\n",
              "      <td id=\"T_af6e7_row15_col4\" class=\"data row15 col4\" >-0.0713</td>\n",
              "      <td id=\"T_af6e7_row15_col5\" class=\"data row15 col5\" >0.4272</td>\n",
              "      <td id=\"T_af6e7_row15_col6\" class=\"data row15 col6\" >0.8412</td>\n",
              "      <td id=\"T_af6e7_row15_col7\" class=\"data row15 col7\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row16\" class=\"row_heading level0 row16\" >dummy</th>\n",
              "      <td id=\"T_af6e7_row16_col0\" class=\"data row16 col0\" >Dummy Regressor</td>\n",
              "      <td id=\"T_af6e7_row16_col1\" class=\"data row16 col1\" >1.5431</td>\n",
              "      <td id=\"T_af6e7_row16_col2\" class=\"data row16 col2\" >3.5423</td>\n",
              "      <td id=\"T_af6e7_row16_col3\" class=\"data row16 col3\" >1.8550</td>\n",
              "      <td id=\"T_af6e7_row16_col4\" class=\"data row16 col4\" >-0.0713</td>\n",
              "      <td id=\"T_af6e7_row16_col5\" class=\"data row16 col5\" >0.4272</td>\n",
              "      <td id=\"T_af6e7_row16_col6\" class=\"data row16 col6\" >0.8412</td>\n",
              "      <td id=\"T_af6e7_row16_col7\" class=\"data row16 col7\" >0.0310</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row17\" class=\"row_heading level0 row17\" >dt</th>\n",
              "      <td id=\"T_af6e7_row17_col0\" class=\"data row17 col0\" >Decision Tree Regressor</td>\n",
              "      <td id=\"T_af6e7_row17_col1\" class=\"data row17 col1\" >1.3867</td>\n",
              "      <td id=\"T_af6e7_row17_col2\" class=\"data row17 col2\" >4.0588</td>\n",
              "      <td id=\"T_af6e7_row17_col3\" class=\"data row17 col3\" >1.9146</td>\n",
              "      <td id=\"T_af6e7_row17_col4\" class=\"data row17 col4\" >-0.2686</td>\n",
              "      <td id=\"T_af6e7_row17_col5\" class=\"data row17 col5\" >0.4516</td>\n",
              "      <td id=\"T_af6e7_row17_col6\" class=\"data row17 col6\" >0.5273</td>\n",
              "      <td id=\"T_af6e7_row17_col7\" class=\"data row17 col7\" >0.0330</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_af6e7_level0_row18\" class=\"row_heading level0 row18\" >par</th>\n",
              "      <td id=\"T_af6e7_row18_col0\" class=\"data row18 col0\" >Passive Aggressive Regressor</td>\n",
              "      <td id=\"T_af6e7_row18_col1\" class=\"data row18 col1\" >1.7230</td>\n",
              "      <td id=\"T_af6e7_row18_col2\" class=\"data row18 col2\" >6.6780</td>\n",
              "      <td id=\"T_af6e7_row18_col3\" class=\"data row18 col3\" >2.4708</td>\n",
              "      <td id=\"T_af6e7_row18_col4\" class=\"data row18 col4\" >-1.1824</td>\n",
              "      <td id=\"T_af6e7_row18_col5\" class=\"data row18 col5\" >0.4163</td>\n",
              "      <td id=\"T_af6e7_row18_col6\" class=\"data row18 col6\" >0.6799</td>\n",
              "      <td id=\"T_af6e7_row18_col7\" class=\"data row18 col7\" >0.0300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/81 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(best)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610,
          "referenced_widgets": [
            "2aa3d395506b4f2997f31143d7f0319a",
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            "09734400a7eb4f8eb3759713e7b0257c",
            "6189726bd13548548906be012a7efccd",
            "8bf9ce76b07b47be95ff59bca467d415",
            "64aa25a40518446b9225432062c7aa9f"
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        },
        "id": "rza8R82wYYCd",
        "outputId": "9b6839c7-84f7-4e2b-8d57-a3ffdad01893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2aa3d395506b4f2997f31143d7f0319a"
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          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf = create_model('rf')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457,
          "referenced_widgets": [
            "81f83c9b45d74f379254dc2713a33ea2",
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            "9966e0952b1a4821b521c1c0c4d5c62a",
            "4db9453fcf2d488bb731cb894536bf64",
            "3a24c161145448fb81a56aa44f3328bd",
            "8d46a2ecf8004b4c9de37c4ce240614a",
            "8980533dbabb4b82b73463be77a323dd",
            "bc09b920cd5549958d3e6bfd26ad3c79",
            "fd9c24fc50ab4e4ca4bb3489961c367e",
            "c038cafdbcc242cc9ca31dd91fdae424"
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        },
        "id": "lKoXjT9eZEbY",
        "outputId": "808fd86b-a61c-4ab7-efed-1ea31ae82e6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f7a12c64160>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_8313f_row10_col0, #T_8313f_row10_col1, #T_8313f_row10_col2, #T_8313f_row10_col3, #T_8313f_row10_col4, #T_8313f_row10_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_8313f\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_8313f_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_8313f_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_8313f_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_8313f_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_8313f_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_8313f_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_8313f_row0_col0\" class=\"data row0 col0\" >0.7092</td>\n",
              "      <td id=\"T_8313f_row0_col1\" class=\"data row0 col1\" >0.9850</td>\n",
              "      <td id=\"T_8313f_row0_col2\" class=\"data row0 col2\" >0.9925</td>\n",
              "      <td id=\"T_8313f_row0_col3\" class=\"data row0 col3\" >0.6454</td>\n",
              "      <td id=\"T_8313f_row0_col4\" class=\"data row0 col4\" >0.2490</td>\n",
              "      <td id=\"T_8313f_row0_col5\" class=\"data row0 col5\" >0.2791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_8313f_row1_col0\" class=\"data row1 col0\" >1.2812</td>\n",
              "      <td id=\"T_8313f_row1_col1\" class=\"data row1 col1\" >4.2663</td>\n",
              "      <td id=\"T_8313f_row1_col2\" class=\"data row1 col2\" >2.0655</td>\n",
              "      <td id=\"T_8313f_row1_col3\" class=\"data row1 col3\" >0.2718</td>\n",
              "      <td id=\"T_8313f_row1_col4\" class=\"data row1 col4\" >0.3192</td>\n",
              "      <td id=\"T_8313f_row1_col5\" class=\"data row1 col5\" >0.2813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_8313f_row2_col0\" class=\"data row2 col0\" >1.2413</td>\n",
              "      <td id=\"T_8313f_row2_col1\" class=\"data row2 col1\" >1.9900</td>\n",
              "      <td id=\"T_8313f_row2_col2\" class=\"data row2 col2\" >1.4107</td>\n",
              "      <td id=\"T_8313f_row2_col3\" class=\"data row2 col3\" >0.3951</td>\n",
              "      <td id=\"T_8313f_row2_col4\" class=\"data row2 col4\" >0.3063</td>\n",
              "      <td id=\"T_8313f_row2_col5\" class=\"data row2 col5\" >0.3694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_8313f_row3_col0\" class=\"data row3 col0\" >1.4592</td>\n",
              "      <td id=\"T_8313f_row3_col1\" class=\"data row3 col1\" >3.8269</td>\n",
              "      <td id=\"T_8313f_row3_col2\" class=\"data row3 col2\" >1.9563</td>\n",
              "      <td id=\"T_8313f_row3_col3\" class=\"data row3 col3\" >-0.0993</td>\n",
              "      <td id=\"T_8313f_row3_col4\" class=\"data row3 col4\" >0.5542</td>\n",
              "      <td id=\"T_8313f_row3_col5\" class=\"data row3 col5\" >1.8106</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_8313f_row4_col0\" class=\"data row4 col0\" >0.7890</td>\n",
              "      <td id=\"T_8313f_row4_col1\" class=\"data row4 col1\" >0.9986</td>\n",
              "      <td id=\"T_8313f_row4_col2\" class=\"data row4 col2\" >0.9993</td>\n",
              "      <td id=\"T_8313f_row4_col3\" class=\"data row4 col3\" >0.5121</td>\n",
              "      <td id=\"T_8313f_row4_col4\" class=\"data row4 col4\" >0.2061</td>\n",
              "      <td id=\"T_8313f_row4_col5\" class=\"data row4 col5\" >0.2255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_8313f_row5_col0\" class=\"data row5 col0\" >0.8963</td>\n",
              "      <td id=\"T_8313f_row5_col1\" class=\"data row5 col1\" >1.2781</td>\n",
              "      <td id=\"T_8313f_row5_col2\" class=\"data row5 col2\" >1.1305</td>\n",
              "      <td id=\"T_8313f_row5_col3\" class=\"data row5 col3\" >0.5417</td>\n",
              "      <td id=\"T_8313f_row5_col4\" class=\"data row5 col4\" >0.2847</td>\n",
              "      <td id=\"T_8313f_row5_col5\" class=\"data row5 col5\" >0.3949</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_8313f_row6_col0\" class=\"data row6 col0\" >0.6883</td>\n",
              "      <td id=\"T_8313f_row6_col1\" class=\"data row6 col1\" >0.6030</td>\n",
              "      <td id=\"T_8313f_row6_col2\" class=\"data row6 col2\" >0.7765</td>\n",
              "      <td id=\"T_8313f_row6_col3\" class=\"data row6 col3\" >0.7445</td>\n",
              "      <td id=\"T_8313f_row6_col4\" class=\"data row6 col4\" >0.1708</td>\n",
              "      <td id=\"T_8313f_row6_col5\" class=\"data row6 col5\" >0.2026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_8313f_row7_col0\" class=\"data row7 col0\" >1.1947</td>\n",
              "      <td id=\"T_8313f_row7_col1\" class=\"data row7 col1\" >1.7826</td>\n",
              "      <td id=\"T_8313f_row7_col2\" class=\"data row7 col2\" >1.3351</td>\n",
              "      <td id=\"T_8313f_row7_col3\" class=\"data row7 col3\" >0.6213</td>\n",
              "      <td id=\"T_8313f_row7_col4\" class=\"data row7 col4\" >0.3997</td>\n",
              "      <td id=\"T_8313f_row7_col5\" class=\"data row7 col5\" >0.9732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_8313f_row8_col0\" class=\"data row8 col0\" >0.7272</td>\n",
              "      <td id=\"T_8313f_row8_col1\" class=\"data row8 col1\" >0.6808</td>\n",
              "      <td id=\"T_8313f_row8_col2\" class=\"data row8 col2\" >0.8251</td>\n",
              "      <td id=\"T_8313f_row8_col3\" class=\"data row8 col3\" >0.5989</td>\n",
              "      <td id=\"T_8313f_row8_col4\" class=\"data row8 col4\" >0.1904</td>\n",
              "      <td id=\"T_8313f_row8_col5\" class=\"data row8 col5\" >0.2454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_8313f_row9_col0\" class=\"data row9 col0\" >1.2737</td>\n",
              "      <td id=\"T_8313f_row9_col1\" class=\"data row9 col1\" >3.1546</td>\n",
              "      <td id=\"T_8313f_row9_col2\" class=\"data row9 col2\" >1.7761</td>\n",
              "      <td id=\"T_8313f_row9_col3\" class=\"data row9 col3\" >0.2925</td>\n",
              "      <td id=\"T_8313f_row9_col4\" class=\"data row9 col4\" >0.3973</td>\n",
              "      <td id=\"T_8313f_row9_col5\" class=\"data row9 col5\" >0.6680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_8313f_row10_col0\" class=\"data row10 col0\" >1.0260</td>\n",
              "      <td id=\"T_8313f_row10_col1\" class=\"data row10 col1\" >1.9566</td>\n",
              "      <td id=\"T_8313f_row10_col2\" class=\"data row10 col2\" >1.3268</td>\n",
              "      <td id=\"T_8313f_row10_col3\" class=\"data row10 col3\" >0.4524</td>\n",
              "      <td id=\"T_8313f_row10_col4\" class=\"data row10 col4\" >0.3078</td>\n",
              "      <td id=\"T_8313f_row10_col5\" class=\"data row10 col5\" >0.5450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8313f_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_8313f_row11_col0\" class=\"data row11 col0\" >0.2767</td>\n",
              "      <td id=\"T_8313f_row11_col1\" class=\"data row11 col1\" >1.2681</td>\n",
              "      <td id=\"T_8313f_row11_col2\" class=\"data row11 col2\" >0.4430</td>\n",
              "      <td id=\"T_8313f_row11_col3\" class=\"data row11 col3\" >0.2345</td>\n",
              "      <td id=\"T_8313f_row11_col4\" class=\"data row11 col4\" >0.1114</td>\n",
              "      <td id=\"T_8313f_row11_col5\" class=\"data row11 col5\" >0.4799</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuned_br = tune_model(rf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513,
          "referenced_widgets": [
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            "b6ff7ec3427244f5bf740c48b3f34237",
            "d4fb3ca7877748f4b7ced687f9a36efb",
            "a398ddd16690461682d7133803fe25d1",
            "9f388d40ea0749719e6d8a932231b809",
            "174884876f074c88af7d1f19fdf5a7b2",
            "64ca8db4a3624ee5bf03533a9eb44ed5"
          ]
        },
        "id": "Bwa19DOcZPOI",
        "outputId": "94826414-9d54-49a5-f985-4842022443f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f7a12a4b100>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_d9c33_row10_col0, #T_d9c33_row10_col1, #T_d9c33_row10_col2, #T_d9c33_row10_col3, #T_d9c33_row10_col4, #T_d9c33_row10_col5 {\n",
              "  background: yellow;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_d9c33\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_d9c33_level0_col0\" class=\"col_heading level0 col0\" >MAE</th>\n",
              "      <th id=\"T_d9c33_level0_col1\" class=\"col_heading level0 col1\" >MSE</th>\n",
              "      <th id=\"T_d9c33_level0_col2\" class=\"col_heading level0 col2\" >RMSE</th>\n",
              "      <th id=\"T_d9c33_level0_col3\" class=\"col_heading level0 col3\" >R2</th>\n",
              "      <th id=\"T_d9c33_level0_col4\" class=\"col_heading level0 col4\" >RMSLE</th>\n",
              "      <th id=\"T_d9c33_level0_col5\" class=\"col_heading level0 col5\" >MAPE</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th class=\"index_name level0\" >Fold</th>\n",
              "      <th class=\"blank col0\" >&nbsp;</th>\n",
              "      <th class=\"blank col1\" >&nbsp;</th>\n",
              "      <th class=\"blank col2\" >&nbsp;</th>\n",
              "      <th class=\"blank col3\" >&nbsp;</th>\n",
              "      <th class=\"blank col4\" >&nbsp;</th>\n",
              "      <th class=\"blank col5\" >&nbsp;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
              "      <td id=\"T_d9c33_row0_col0\" class=\"data row0 col0\" >0.6366</td>\n",
              "      <td id=\"T_d9c33_row0_col1\" class=\"data row0 col1\" >0.6387</td>\n",
              "      <td id=\"T_d9c33_row0_col2\" class=\"data row0 col2\" >0.7992</td>\n",
              "      <td id=\"T_d9c33_row0_col3\" class=\"data row0 col3\" >0.7701</td>\n",
              "      <td id=\"T_d9c33_row0_col4\" class=\"data row0 col4\" >0.2027</td>\n",
              "      <td id=\"T_d9c33_row0_col5\" class=\"data row0 col5\" >0.2344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
              "      <td id=\"T_d9c33_row1_col0\" class=\"data row1 col0\" >1.3237</td>\n",
              "      <td id=\"T_d9c33_row1_col1\" class=\"data row1 col1\" >4.4410</td>\n",
              "      <td id=\"T_d9c33_row1_col2\" class=\"data row1 col2\" >2.1074</td>\n",
              "      <td id=\"T_d9c33_row1_col3\" class=\"data row1 col3\" >0.2420</td>\n",
              "      <td id=\"T_d9c33_row1_col4\" class=\"data row1 col4\" >0.3241</td>\n",
              "      <td id=\"T_d9c33_row1_col5\" class=\"data row1 col5\" >0.3007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
              "      <td id=\"T_d9c33_row2_col0\" class=\"data row2 col0\" >1.2116</td>\n",
              "      <td id=\"T_d9c33_row2_col1\" class=\"data row2 col1\" >1.9241</td>\n",
              "      <td id=\"T_d9c33_row2_col2\" class=\"data row2 col2\" >1.3871</td>\n",
              "      <td id=\"T_d9c33_row2_col3\" class=\"data row2 col3\" >0.4152</td>\n",
              "      <td id=\"T_d9c33_row2_col4\" class=\"data row2 col4\" >0.3131</td>\n",
              "      <td id=\"T_d9c33_row2_col5\" class=\"data row2 col5\" >0.3998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
              "      <td id=\"T_d9c33_row3_col0\" class=\"data row3 col0\" >1.4297</td>\n",
              "      <td id=\"T_d9c33_row3_col1\" class=\"data row3 col1\" >3.7581</td>\n",
              "      <td id=\"T_d9c33_row3_col2\" class=\"data row3 col2\" >1.9386</td>\n",
              "      <td id=\"T_d9c33_row3_col3\" class=\"data row3 col3\" >-0.0795</td>\n",
              "      <td id=\"T_d9c33_row3_col4\" class=\"data row3 col4\" >0.5532</td>\n",
              "      <td id=\"T_d9c33_row3_col5\" class=\"data row3 col5\" >1.8107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
              "      <td id=\"T_d9c33_row4_col0\" class=\"data row4 col0\" >0.7863</td>\n",
              "      <td id=\"T_d9c33_row4_col1\" class=\"data row4 col1\" >0.8559</td>\n",
              "      <td id=\"T_d9c33_row4_col2\" class=\"data row4 col2\" >0.9252</td>\n",
              "      <td id=\"T_d9c33_row4_col3\" class=\"data row4 col3\" >0.5818</td>\n",
              "      <td id=\"T_d9c33_row4_col4\" class=\"data row4 col4\" >0.1902</td>\n",
              "      <td id=\"T_d9c33_row4_col5\" class=\"data row4 col5\" >0.2203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
              "      <td id=\"T_d9c33_row5_col0\" class=\"data row5 col0\" >0.9263</td>\n",
              "      <td id=\"T_d9c33_row5_col1\" class=\"data row5 col1\" >1.2782</td>\n",
              "      <td id=\"T_d9c33_row5_col2\" class=\"data row5 col2\" >1.1306</td>\n",
              "      <td id=\"T_d9c33_row5_col3\" class=\"data row5 col3\" >0.5416</td>\n",
              "      <td id=\"T_d9c33_row5_col4\" class=\"data row5 col4\" >0.2939</td>\n",
              "      <td id=\"T_d9c33_row5_col5\" class=\"data row5 col5\" >0.4191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
              "      <td id=\"T_d9c33_row6_col0\" class=\"data row6 col0\" >0.7124</td>\n",
              "      <td id=\"T_d9c33_row6_col1\" class=\"data row6 col1\" >0.6990</td>\n",
              "      <td id=\"T_d9c33_row6_col2\" class=\"data row6 col2\" >0.8361</td>\n",
              "      <td id=\"T_d9c33_row6_col3\" class=\"data row6 col3\" >0.7038</td>\n",
              "      <td id=\"T_d9c33_row6_col4\" class=\"data row6 col4\" >0.1751</td>\n",
              "      <td id=\"T_d9c33_row6_col5\" class=\"data row6 col5\" >0.2030</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
              "      <td id=\"T_d9c33_row7_col0\" class=\"data row7 col0\" >1.2945</td>\n",
              "      <td id=\"T_d9c33_row7_col1\" class=\"data row7 col1\" >2.0977</td>\n",
              "      <td id=\"T_d9c33_row7_col2\" class=\"data row7 col2\" >1.4484</td>\n",
              "      <td id=\"T_d9c33_row7_col3\" class=\"data row7 col3\" >0.5544</td>\n",
              "      <td id=\"T_d9c33_row7_col4\" class=\"data row7 col4\" >0.4082</td>\n",
              "      <td id=\"T_d9c33_row7_col5\" class=\"data row7 col5\" >0.9749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
              "      <td id=\"T_d9c33_row8_col0\" class=\"data row8 col0\" >0.7060</td>\n",
              "      <td id=\"T_d9c33_row8_col1\" class=\"data row8 col1\" >0.8826</td>\n",
              "      <td id=\"T_d9c33_row8_col2\" class=\"data row8 col2\" >0.9395</td>\n",
              "      <td id=\"T_d9c33_row8_col3\" class=\"data row8 col3\" >0.4800</td>\n",
              "      <td id=\"T_d9c33_row8_col4\" class=\"data row8 col4\" >0.2199</td>\n",
              "      <td id=\"T_d9c33_row8_col5\" class=\"data row8 col5\" >0.2622</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
              "      <td id=\"T_d9c33_row9_col0\" class=\"data row9 col0\" >1.2613</td>\n",
              "      <td id=\"T_d9c33_row9_col1\" class=\"data row9 col1\" >3.0700</td>\n",
              "      <td id=\"T_d9c33_row9_col2\" class=\"data row9 col2\" >1.7521</td>\n",
              "      <td id=\"T_d9c33_row9_col3\" class=\"data row9 col3\" >0.3115</td>\n",
              "      <td id=\"T_d9c33_row9_col4\" class=\"data row9 col4\" >0.3993</td>\n",
              "      <td id=\"T_d9c33_row9_col5\" class=\"data row9 col5\" >0.6912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row10\" class=\"row_heading level0 row10\" >Mean</th>\n",
              "      <td id=\"T_d9c33_row10_col0\" class=\"data row10 col0\" >1.0288</td>\n",
              "      <td id=\"T_d9c33_row10_col1\" class=\"data row10 col1\" >1.9645</td>\n",
              "      <td id=\"T_d9c33_row10_col2\" class=\"data row10 col2\" >1.3264</td>\n",
              "      <td id=\"T_d9c33_row10_col3\" class=\"data row10 col3\" >0.4521</td>\n",
              "      <td id=\"T_d9c33_row10_col4\" class=\"data row10 col4\" >0.3080</td>\n",
              "      <td id=\"T_d9c33_row10_col5\" class=\"data row10 col5\" >0.5516</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_d9c33_level0_row11\" class=\"row_heading level0 row11\" >Std</th>\n",
              "      <td id=\"T_d9c33_row11_col0\" class=\"data row11 col0\" >0.2887</td>\n",
              "      <td id=\"T_d9c33_row11_col1\" class=\"data row11 col1\" >1.2970</td>\n",
              "      <td id=\"T_d9c33_row11_col2\" class=\"data row11 col2\" >0.4530</td>\n",
              "      <td id=\"T_d9c33_row11_col3\" class=\"data row11 col3\" >0.2342</td>\n",
              "      <td id=\"T_d9c33_row11_col4\" class=\"data row11 col4\" >0.1137</td>\n",
              "      <td id=\"T_d9c33_row11_col5\" class=\"data row11 col5\" >0.4799</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Processing:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4fe5344453be4abc943dfffef423bf75"
            }
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/b3e629b1971e1542/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(tuned_br, plot = 'error')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "IYEmulePYX-m",
        "outputId": "42efec64-665a-40f9-8cec-34c7eb660c82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}