diff --git "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb" "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb"
index 07a3915..8d498dd 100644
--- "a/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb"	
+++ "b/5.- Aprendizaje autom\303\241tico/5.- Sesi\303\263n S\303\241bado.ipynb"	
@@ -86,12 +86,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[BernoulliRBM] Iteration 1, pseudo-likelihood = -1.11, time = 0.02s\n",
+      "[BernoulliRBM] Iteration 1, pseudo-likelihood = -1.11, time = 0.03s\n",
       "[BernoulliRBM] Iteration 2, pseudo-likelihood = -1.23, time = 0.01s\n",
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       "[BernoulliRBM] Iteration 5, pseudo-likelihood = -1.03, time = 0.01s\n",
-      "[BernoulliRBM] Iteration 6, pseudo-likelihood = -1.15, time = 0.01s\n",
+      "[BernoulliRBM] Iteration 6, pseudo-likelihood = -1.15, time = 0.00s\n",
       "[BernoulliRBM] Iteration 7, pseudo-likelihood = -1.20, time = 0.01s\n",
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@@ -115,12 +115,12 @@
       "[BernoulliRBM] Iteration 27, pseudo-likelihood = -1.23, time = 0.01s\n",
       "[BernoulliRBM] Iteration 28, pseudo-likelihood = -1.14, time = 0.01s\n",
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-      "[BernoulliRBM] Iteration 30, pseudo-likelihood = -1.21, time = 0.01s\n",
+      "[BernoulliRBM] Iteration 30, pseudo-likelihood = -1.21, time = 0.00s\n",
       "[BernoulliRBM] Iteration 31, pseudo-likelihood = -1.12, time = 0.01s\n",
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-      "[BernoulliRBM] Iteration 34, pseudo-likelihood = -1.13, time = 0.01s\n",
-      "[BernoulliRBM] Iteration 35, pseudo-likelihood = -1.20, time = 0.01s\n",
+      "[BernoulliRBM] Iteration 34, pseudo-likelihood = -1.13, time = 0.00s\n",
+      "[BernoulliRBM] Iteration 35, pseudo-likelihood = -1.20, time = 0.00s\n",
       "[BernoulliRBM] Iteration 36, pseudo-likelihood = -1.13, time = 0.01s\n",
       "[BernoulliRBM] Iteration 37, pseudo-likelihood = -1.12, time = 0.01s\n",
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@@ -150,7 +150,7 @@
       "[BernoulliRBM] Iteration 62, pseudo-likelihood = -1.18, time = 0.01s\n",
       "[BernoulliRBM] Iteration 63, pseudo-likelihood = -1.12, time = 0.01s\n",
       "[BernoulliRBM] Iteration 64, pseudo-likelihood = -1.19, time = 0.01s\n",
-      "[BernoulliRBM] Iteration 65, pseudo-likelihood = -1.16, time = 0.01s\n",
+      "[BernoulliRBM] Iteration 65, pseudo-likelihood = -1.16, time = 0.00s\n",
       "[BernoulliRBM] Iteration 66, pseudo-likelihood = -1.13, time = 0.01s\n",
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       "[BernoulliRBM] Iteration 68, pseudo-likelihood = -1.06, time = 0.01s\n",
@@ -261,7 +261,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x11939b828>"
+       "<matplotlib.text.Text at 0x1191de780>"
       ]
      },
      "execution_count": 5,
@@ -272,7 +272,7 @@
      "data": {
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OxMzMalep3VBf6Wx7RFxZmXDMzKwWdeVpqMOAO9P1CcAi4Nk8gjIzs9pSarIYA4yLiNcA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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x119199940>"
+       "<matplotlib.figure.Figure at 0x11919f940>"
       ]
      },
      "metadata": {},
@@ -282,7 +282,7 @@
    "source": [
     "%matplotlib inline\n",
     "from matplotlib import pyplot as plt\n",
-    "from numpy import arange, array\n",
+    "from numpy import arange, array, delete\n",
     "nn_scores_range = arange(0.67,0.71,0.002)\n",
     "df_good = df_testing[df_testing['is_good']==1] \n",
     "df_bad = df_testing[df_testing['is_good']!=1]\n",
@@ -676,35 +676,35 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "      <th>4</th>\n",
-       "      <th>5</th>\n",
-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "      <th>9</th>\n",
-       "      <th>10</th>\n",
-       "      <th>11</th>\n",
-       "      <th>12</th>\n",
-       "      <th>13</th>\n",
-       "      <th>14</th>\n",
-       "      <th>15</th>\n",
-       "      <th>16</th>\n",
-       "      <th>17</th>\n",
-       "      <th>18</th>\n",
+       "      <th>0.67</th>\n",
+       "      <th>0.672</th>\n",
+       "      <th>0.674</th>\n",
+       "      <th>0.676</th>\n",
+       "      <th>0.678</th>\n",
+       "      <th>0.68</th>\n",
+       "      <th>0.682</th>\n",
+       "      <th>0.684</th>\n",
+       "      <th>0.686</th>\n",
+       "      <th>0.688</th>\n",
+       "      <th>0.69</th>\n",
+       "      <th>0.692</th>\n",
+       "      <th>0.694</th>\n",
+       "      <th>0.696</th>\n",
+       "      <th>0.698</th>\n",
+       "      <th>0.7</th>\n",
+       "      <th>0.702</th>\n",
+       "      <th>0.704</th>\n",
+       "      <th>0.706</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
+       "      <td>100.00000</td>\n",
        "      <td>100.000000</td>\n",
        "      <td>100.000000</td>\n",
        "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
-       "      <td>100.000000</td>\n",
+       "      <td>100.00000</td>\n",
        "      <td>100.000000</td>\n",
        "      <td>100.000000</td>\n",
        "      <td>100.000000</td>\n",
@@ -722,212 +722,212 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
-       "      <td>-6050.000000</td>\n",
-       "      <td>3220.000000</td>\n",
-       "      <td>22300.000000</td>\n",
-       "      <td>58210.000000</td>\n",
-       "      <td>70240.000000</td>\n",
-       "      <td>96040.000000</td>\n",
-       "      <td>110200.000000</td>\n",
-       "      <td>147850.000000</td>\n",
-       "      <td>169540.000000</td>\n",
-       "      <td>158740.000000</td>\n",
-       "      <td>160600.000000</td>\n",
-       "      <td>195520.000000</td>\n",
-       "      <td>130120.000000</td>\n",
-       "      <td>74230.00000</td>\n",
-       "      <td>-28160.000000</td>\n",
-       "      <td>-199820.000000</td>\n",
-       "      <td>-307190.000000</td>\n",
-       "      <td>-400280.000000</td>\n",
-       "      <td>-464660.000000</td>\n",
+       "      <td>11800.00000</td>\n",
+       "      <td>18910.000000</td>\n",
+       "      <td>35380.000000</td>\n",
+       "      <td>70930.000000</td>\n",
+       "      <td>82000.00000</td>\n",
+       "      <td>107410.000000</td>\n",
+       "      <td>122860.000000</td>\n",
+       "      <td>157210.000000</td>\n",
+       "      <td>176770.000000</td>\n",
+       "      <td>165010.000000</td>\n",
+       "      <td>168910.000000</td>\n",
+       "      <td>201490.000000</td>\n",
+       "      <td>136210.000000</td>\n",
+       "      <td>85210.00000</td>\n",
+       "      <td>-18350.000000</td>\n",
+       "      <td>-198110.000000</td>\n",
+       "      <td>-301880.000000</td>\n",
+       "      <td>-397520.000000</td>\n",
+       "      <td>-463220.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
-       "      <td>130997.137373</td>\n",
-       "      <td>129294.322451</td>\n",
-       "      <td>126791.525384</td>\n",
-       "      <td>122062.767013</td>\n",
-       "      <td>122234.260931</td>\n",
-       "      <td>119392.309787</td>\n",
-       "      <td>115904.349279</td>\n",
-       "      <td>111208.546925</td>\n",
-       "      <td>104215.144598</td>\n",
-       "      <td>97942.636272</td>\n",
-       "      <td>92562.509591</td>\n",
-       "      <td>82057.319213</td>\n",
-       "      <td>78994.729398</td>\n",
-       "      <td>67671.79485</td>\n",
-       "      <td>52958.096643</td>\n",
-       "      <td>50046.923436</td>\n",
-       "      <td>38465.699006</td>\n",
-       "      <td>23890.355604</td>\n",
-       "      <td>14792.995886</td>\n",
+       "      <td>117071.85051</td>\n",
+       "      <td>116872.467401</td>\n",
+       "      <td>115618.825142</td>\n",
+       "      <td>114400.965508</td>\n",
+       "      <td>114642.52741</td>\n",
+       "      <td>112335.904565</td>\n",
+       "      <td>110863.372097</td>\n",
+       "      <td>109266.359291</td>\n",
+       "      <td>103756.791673</td>\n",
+       "      <td>99565.469553</td>\n",
+       "      <td>89796.188419</td>\n",
+       "      <td>83636.719861</td>\n",
+       "      <td>81199.255482</td>\n",
+       "      <td>68276.51666</td>\n",
+       "      <td>60846.117378</td>\n",
+       "      <td>48579.185591</td>\n",
+       "      <td>38470.579077</td>\n",
+       "      <td>21194.853578</td>\n",
+       "      <td>14184.626762</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
-       "      <td>-365000.000000</td>\n",
-       "      <td>-338000.000000</td>\n",
-       "      <td>-311000.000000</td>\n",
-       "      <td>-266000.000000</td>\n",
-       "      <td>-257000.000000</td>\n",
+       "      <td>-260000.00000</td>\n",
+       "      <td>-251000.000000</td>\n",
        "      <td>-233000.000000</td>\n",
-       "      <td>-206000.000000</td>\n",
-       "      <td>-116000.000000</td>\n",
-       "      <td>-53000.000000</td>\n",
-       "      <td>-47000.000000</td>\n",
-       "      <td>-77000.000000</td>\n",
-       "      <td>-14000.000000</td>\n",
-       "      <td>-59000.000000</td>\n",
-       "      <td>-71000.00000</td>\n",
-       "      <td>-155000.000000</td>\n",
-       "      <td>-293000.000000</td>\n",
-       "      <td>-392000.000000</td>\n",
-       "      <td>-452000.000000</td>\n",
-       "      <td>-500000.000000</td>\n",
+       "      <td>-188000.000000</td>\n",
+       "      <td>-188000.00000</td>\n",
+       "      <td>-173000.000000</td>\n",
+       "      <td>-167000.000000</td>\n",
+       "      <td>-152000.000000</td>\n",
+       "      <td>-101000.000000</td>\n",
+       "      <td>-62000.000000</td>\n",
+       "      <td>-17000.000000</td>\n",
+       "      <td>25000.000000</td>\n",
+       "      <td>-62000.000000</td>\n",
+       "      <td>-77000.00000</td>\n",
+       "      <td>-182000.000000</td>\n",
+       "      <td>-314000.000000</td>\n",
+       "      <td>-407000.000000</td>\n",
+       "      <td>-440000.000000</td>\n",
+       "      <td>-488000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>-95000.000000</td>\n",
-       "      <td>-88250.000000</td>\n",
-       "      <td>-71750.000000</td>\n",
-       "      <td>-17750.000000</td>\n",
-       "      <td>-12500.000000</td>\n",
-       "      <td>18250.000000</td>\n",
-       "      <td>31000.000000</td>\n",
-       "      <td>73000.000000</td>\n",
-       "      <td>91000.000000</td>\n",
-       "      <td>91000.000000</td>\n",
-       "      <td>99250.000000</td>\n",
-       "      <td>144250.000000</td>\n",
+       "      <td>-50000.00000</td>\n",
+       "      <td>-42500.000000</td>\n",
+       "      <td>-32000.000000</td>\n",
+       "      <td>1750.000000</td>\n",
+       "      <td>12250.00000</td>\n",
+       "      <td>35500.000000</td>\n",
+       "      <td>46000.000000</td>\n",
+       "      <td>97000.000000</td>\n",
+       "      <td>126250.000000</td>\n",
+       "      <td>109000.000000</td>\n",
+       "      <td>94000.000000</td>\n",
+       "      <td>133000.000000</td>\n",
        "      <td>76000.000000</td>\n",
-       "      <td>30250.00000</td>\n",
-       "      <td>-56750.000000</td>\n",
+       "      <td>44500.00000</td>\n",
+       "      <td>-59750.000000</td>\n",
        "      <td>-233750.000000</td>\n",
-       "      <td>-335000.000000</td>\n",
-       "      <td>-416000.000000</td>\n",
+       "      <td>-323750.000000</td>\n",
+       "      <td>-410000.000000</td>\n",
        "      <td>-476000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>-5000.000000</td>\n",
-       "      <td>1000.000000</td>\n",
-       "      <td>19000.000000</td>\n",
-       "      <td>61000.000000</td>\n",
-       "      <td>67000.000000</td>\n",
-       "      <td>103000.000000</td>\n",
-       "      <td>112000.000000</td>\n",
-       "      <td>152500.000000</td>\n",
-       "      <td>160000.000000</td>\n",
+       "      <td>-5000.00000</td>\n",
+       "      <td>13000.000000</td>\n",
+       "      <td>34000.000000</td>\n",
+       "      <td>73000.000000</td>\n",
+       "      <td>88000.00000</td>\n",
+       "      <td>118000.000000</td>\n",
+       "      <td>133000.000000</td>\n",
+       "      <td>157000.000000</td>\n",
+       "      <td>178000.000000</td>\n",
+       "      <td>172000.000000</td>\n",
+       "      <td>173500.000000</td>\n",
+       "      <td>209500.000000</td>\n",
        "      <td>146500.000000</td>\n",
-       "      <td>166000.000000</td>\n",
-       "      <td>188500.000000</td>\n",
-       "      <td>124000.000000</td>\n",
-       "      <td>73000.00000</td>\n",
-       "      <td>-27500.000000</td>\n",
-       "      <td>-203000.000000</td>\n",
-       "      <td>-309500.000000</td>\n",
+       "      <td>89500.00000</td>\n",
+       "      <td>-20000.000000</td>\n",
+       "      <td>-191000.000000</td>\n",
+       "      <td>-302000.000000</td>\n",
        "      <td>-398000.000000</td>\n",
        "      <td>-464000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>85000.000000</td>\n",
-       "      <td>97750.000000</td>\n",
-       "      <td>115750.000000</td>\n",
-       "      <td>145000.000000</td>\n",
-       "      <td>163000.000000</td>\n",
-       "      <td>175750.000000</td>\n",
-       "      <td>187000.000000</td>\n",
-       "      <td>222250.000000</td>\n",
+       "      <td>85000.00000</td>\n",
+       "      <td>87250.000000</td>\n",
+       "      <td>103000.000000</td>\n",
+       "      <td>133750.000000</td>\n",
+       "      <td>148000.00000</td>\n",
+       "      <td>172750.000000</td>\n",
+       "      <td>184000.000000</td>\n",
+       "      <td>221500.000000</td>\n",
        "      <td>236500.000000</td>\n",
-       "      <td>235000.000000</td>\n",
-       "      <td>220750.000000</td>\n",
-       "      <td>253750.000000</td>\n",
-       "      <td>176500.000000</td>\n",
-       "      <td>115000.00000</td>\n",
-       "      <td>1750.000000</td>\n",
-       "      <td>-167000.000000</td>\n",
-       "      <td>-283250.000000</td>\n",
-       "      <td>-380000.000000</td>\n",
+       "      <td>226000.000000</td>\n",
+       "      <td>229000.000000</td>\n",
+       "      <td>263500.000000</td>\n",
+       "      <td>193000.000000</td>\n",
+       "      <td>130000.00000</td>\n",
+       "      <td>28000.000000</td>\n",
+       "      <td>-164000.000000</td>\n",
+       "      <td>-280250.000000</td>\n",
+       "      <td>-386000.000000</td>\n",
        "      <td>-452000.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
-       "      <td>280000.000000</td>\n",
-       "      <td>280000.000000</td>\n",
+       "      <td>280000.00000</td>\n",
        "      <td>298000.000000</td>\n",
        "      <td>307000.000000</td>\n",
-       "      <td>325000.000000</td>\n",
        "      <td>361000.000000</td>\n",
-       "      <td>340000.000000</td>\n",
+       "      <td>388000.00000</td>\n",
+       "      <td>427000.000000</td>\n",
+       "      <td>439000.000000</td>\n",
+       "      <td>466000.000000</td>\n",
+       "      <td>448000.000000</td>\n",
        "      <td>406000.000000</td>\n",
-       "      <td>400000.000000</td>\n",
-       "      <td>373000.000000</td>\n",
-       "      <td>355000.000000</td>\n",
-       "      <td>394000.000000</td>\n",
-       "      <td>391000.000000</td>\n",
-       "      <td>277000.00000</td>\n",
-       "      <td>124000.000000</td>\n",
-       "      <td>-32000.000000</td>\n",
-       "      <td>-182000.000000</td>\n",
-       "      <td>-344000.000000</td>\n",
-       "      <td>-422000.000000</td>\n",
+       "      <td>367000.000000</td>\n",
+       "      <td>364000.000000</td>\n",
+       "      <td>325000.000000</td>\n",
+       "      <td>241000.00000</td>\n",
+       "      <td>103000.000000</td>\n",
+       "      <td>-80000.000000</td>\n",
+       "      <td>-194000.000000</td>\n",
+       "      <td>-332000.000000</td>\n",
+       "      <td>-428000.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "                  0              1              2              3   \\\n",
-       "count     100.000000     100.000000     100.000000     100.000000   \n",
-       "mean    -6050.000000    3220.000000   22300.000000   58210.000000   \n",
-       "std    130997.137373  129294.322451  126791.525384  122062.767013   \n",
-       "min   -365000.000000 -338000.000000 -311000.000000 -266000.000000   \n",
-       "25%    -95000.000000  -88250.000000  -71750.000000  -17750.000000   \n",
-       "50%     -5000.000000    1000.000000   19000.000000   61000.000000   \n",
-       "75%     85000.000000   97750.000000  115750.000000  145000.000000   \n",
-       "max    280000.000000  280000.000000  298000.000000  307000.000000   \n",
+       "              0.670          0.672          0.674          0.676  \\\n",
+       "count     100.00000     100.000000     100.000000     100.000000   \n",
+       "mean    11800.00000   18910.000000   35380.000000   70930.000000   \n",
+       "std    117071.85051  116872.467401  115618.825142  114400.965508   \n",
+       "min   -260000.00000 -251000.000000 -233000.000000 -188000.000000   \n",
+       "25%    -50000.00000  -42500.000000  -32000.000000    1750.000000   \n",
+       "50%     -5000.00000   13000.000000   34000.000000   73000.000000   \n",
+       "75%     85000.00000   87250.000000  103000.000000  133750.000000   \n",
+       "max    280000.00000  298000.000000  307000.000000  361000.000000   \n",
        "\n",
-       "                  4              5              6              7   \\\n",
-       "count     100.000000     100.000000     100.000000     100.000000   \n",
-       "mean    70240.000000   96040.000000  110200.000000  147850.000000   \n",
-       "std    122234.260931  119392.309787  115904.349279  111208.546925   \n",
-       "min   -257000.000000 -233000.000000 -206000.000000 -116000.000000   \n",
-       "25%    -12500.000000   18250.000000   31000.000000   73000.000000   \n",
-       "50%     67000.000000  103000.000000  112000.000000  152500.000000   \n",
-       "75%    163000.000000  175750.000000  187000.000000  222250.000000   \n",
-       "max    325000.000000  361000.000000  340000.000000  406000.000000   \n",
+       "              0.678          0.680          0.682          0.684  \\\n",
+       "count     100.00000     100.000000     100.000000     100.000000   \n",
+       "mean    82000.00000  107410.000000  122860.000000  157210.000000   \n",
+       "std    114642.52741  112335.904565  110863.372097  109266.359291   \n",
+       "min   -188000.00000 -173000.000000 -167000.000000 -152000.000000   \n",
+       "25%     12250.00000   35500.000000   46000.000000   97000.000000   \n",
+       "50%     88000.00000  118000.000000  133000.000000  157000.000000   \n",
+       "75%    148000.00000  172750.000000  184000.000000  221500.000000   \n",
+       "max    388000.00000  427000.000000  439000.000000  466000.000000   \n",
        "\n",
-       "                  8              9              10             11  \\\n",
+       "               0.686          0.688          0.690          0.692  \\\n",
        "count     100.000000     100.000000     100.000000     100.000000   \n",
-       "mean   169540.000000  158740.000000  160600.000000  195520.000000   \n",
-       "std    104215.144598   97942.636272   92562.509591   82057.319213   \n",
-       "min    -53000.000000  -47000.000000  -77000.000000  -14000.000000   \n",
-       "25%     91000.000000   91000.000000   99250.000000  144250.000000   \n",
-       "50%    160000.000000  146500.000000  166000.000000  188500.000000   \n",
-       "75%    236500.000000  235000.000000  220750.000000  253750.000000   \n",
-       "max    400000.000000  373000.000000  355000.000000  394000.000000   \n",
+       "mean   176770.000000  165010.000000  168910.000000  201490.000000   \n",
+       "std    103756.791673   99565.469553   89796.188419   83636.719861   \n",
+       "min   -101000.000000  -62000.000000  -17000.000000   25000.000000   \n",
+       "25%    126250.000000  109000.000000   94000.000000  133000.000000   \n",
+       "50%    178000.000000  172000.000000  173500.000000  209500.000000   \n",
+       "75%    236500.000000  226000.000000  229000.000000  263500.000000   \n",
+       "max    448000.000000  406000.000000  367000.000000  364000.000000   \n",
        "\n",
-       "                  12            13             14             15  \\\n",
+       "               0.694         0.696          0.698          0.700  \\\n",
        "count     100.000000     100.00000     100.000000     100.000000   \n",
-       "mean   130120.000000   74230.00000  -28160.000000 -199820.000000   \n",
-       "std     78994.729398   67671.79485   52958.096643   50046.923436   \n",
-       "min    -59000.000000  -71000.00000 -155000.000000 -293000.000000   \n",
-       "25%     76000.000000   30250.00000  -56750.000000 -233750.000000   \n",
-       "50%    124000.000000   73000.00000  -27500.000000 -203000.000000   \n",
-       "75%    176500.000000  115000.00000    1750.000000 -167000.000000   \n",
-       "max    391000.000000  277000.00000  124000.000000  -32000.000000   \n",
+       "mean   136210.000000   85210.00000  -18350.000000 -198110.000000   \n",
+       "std     81199.255482   68276.51666   60846.117378   48579.185591   \n",
+       "min    -62000.000000  -77000.00000 -182000.000000 -314000.000000   \n",
+       "25%     76000.000000   44500.00000  -59750.000000 -233750.000000   \n",
+       "50%    146500.000000   89500.00000  -20000.000000 -191000.000000   \n",
+       "75%    193000.000000  130000.00000   28000.000000 -164000.000000   \n",
+       "max    325000.000000  241000.00000  103000.000000  -80000.000000   \n",
        "\n",
-       "                  16             17             18  \n",
+       "               0.702          0.704          0.706  \n",
        "count     100.000000     100.000000     100.000000  \n",
-       "mean  -307190.000000 -400280.000000 -464660.000000  \n",
-       "std     38465.699006   23890.355604   14792.995886  \n",
-       "min   -392000.000000 -452000.000000 -500000.000000  \n",
-       "25%   -335000.000000 -416000.000000 -476000.000000  \n",
-       "50%   -309500.000000 -398000.000000 -464000.000000  \n",
-       "75%   -283250.000000 -380000.000000 -452000.000000  \n",
-       "max   -182000.000000 -344000.000000 -422000.000000  "
+       "mean  -301880.000000 -397520.000000 -463220.000000  \n",
+       "std     38470.579077   21194.853578   14184.626762  \n",
+       "min   -407000.000000 -440000.000000 -488000.000000  \n",
+       "25%   -323750.000000 -410000.000000 -476000.000000  \n",
+       "50%   -302000.000000 -398000.000000 -464000.000000  \n",
+       "75%   -280250.000000 -386000.000000 -452000.000000  \n",
+       "max   -194000.000000 -332000.000000 -428000.000000  "
       ]
      },
      "execution_count": 7,
@@ -945,9 +945,10 @@
     "    bs_f = bootstrapped_utility(df_testing=df_testing,varname='neural_net', bins = nn_scores_range)\n",
     "    bs_df_tmp = DataFrame(bs_f)\n",
     "    bs_df_tmp = bs_df_tmp.transpose()\n",
-    "    bs_df.loc[n]=bs_df_tmp.loc[0]      \n",
+    "    bs_df.loc[n]=bs_df_tmp.loc[0]    \n",
     "bs_results=bs_df.describe()\n",
-    "bs_results"
+    "bs_results.columns=delete(nn_scores_range,-1)\n",
+    "bs_results\n"
    ]
   },
   {
@@ -960,7 +961,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.text.Text at 0x11bc2bd30>"
+       "<matplotlib.text.Text at 0x11bbc49b0>"
       ]
      },
      "execution_count": 8,
@@ -969,9 +970,9 @@
     },
     {
      "data": {
-      "image/png": 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fbQNyNAWMs7Ot2snChXDGGbHph0tvhwxaqrpeREYCXwua/hnLChki8ldsxNNV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\nWmZmNmC40jIzswHj/wFa8R33YZkqUwAAAABJRU5ErkJggg==\n",
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syktG0Nqb77WrgEFRfLiI9BKRt0XkCxH5TERuCtrbi8hUEVkkIlNEpG3Ca+4SkcUislBE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\nnwAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1193332b0>"
+       "<matplotlib.figure.Figure at 0x10c7aa438>"
       ]
      },
      "metadata": {},