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karbartolome committed May 12, 2024
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}
],
"source": [
"df = (pd.read_csv('df_german_credit.csv')\n",
"df = (pd.read_csv('https://raw.githubusercontent.com/karbartolome/workshops/main/20240513-cimbage-calibracion/df_german_credit.csv')\n",
" .drop('Unnamed: 0', axis=1)\n",
" .assign(Risk = lambda x: np.where(x['Risk']=='good',0,1))\n",
")\n",
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105 changes: 105 additions & 0 deletions 20240513-uba-calibracion/requirements.txt
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altgraph @ file:///System/Volumes/Data/SWE/Apps/DT/BuildRoots/BuildRoot7/ActiveBuildRoot/Library/Caches/com.apple.xbs/Sources/python3/python3-133.100.1.1/altgraph-0.17.2-py2.py3-none-any.whl
anyio==4.3.0
appnope==0.1.4
argon2-cffi==23.1.0
argon2-cffi-bindings==21.2.0
arrow==1.3.0
asttokens==2.4.1
async-lru==2.0.4
attrs==23.2.0
Babel==2.14.0
beautifulsoup4==4.12.3
bleach==6.1.0
certifi==2024.2.2
cffi==1.16.0
charset-normalizer==3.3.2
comm==0.2.2
debugpy==1.8.1
decorator==5.1.1
defusedxml==0.7.1
exceptiongroup==1.2.0
executing==2.0.1
fastjsonschema==2.19.1
fqdn==1.5.1
future @ file:///System/Volumes/Data/SWE/Apps/DT/BuildRoots/BuildRoot7/ActiveBuildRoot/Library/Caches/com.apple.xbs/Sources/python3/python3-133.100.1.1/future-0.18.2-py3-none-any.whl
h11==0.14.0
httpcore==1.0.5
httpx==0.27.0
idna==3.7
importlib_metadata==7.1.0
ipykernel==6.29.4
ipython==8.18.1
ipywidgets==8.1.2
isoduration==20.11.0
jedi==0.19.1
Jinja2==3.1.3
json5==0.9.25
jsonpointer==2.4
jsonschema==4.21.1
jsonschema-specifications==2023.12.1
jupyter==1.0.0
jupyter-console==6.6.3
jupyter-events==0.10.0
jupyter-lsp==2.2.5
jupyter_client==8.6.1
jupyter_core==5.7.2
jupyter_server==2.14.0
jupyter_server_terminals==0.5.3
jupyterlab==4.1.6
jupyterlab_pygments==0.3.0
jupyterlab_server==2.26.0
jupyterlab_widgets==3.0.10
macholib @ file:///System/Volumes/Data/SWE/Apps/DT/BuildRoots/BuildRoot7/ActiveBuildRoot/Library/Caches/com.apple.xbs/Sources/python3/python3-133.100.1.1/macholib-1.15.2-py2.py3-none-any.whl
MarkupSafe==2.1.5
matplotlib-inline==0.1.6
mistune==3.0.2
nbclient==0.10.0
nbconvert==7.16.3
nbformat==5.10.4
nest-asyncio==1.6.0
notebook==7.1.2
notebook_shim==0.2.4
overrides==7.7.0
packaging==24.0
pandocfilters==1.5.1
parso==0.8.4
pexpect==4.9.0
platformdirs==4.2.0
prometheus_client==0.20.0
prompt-toolkit==3.0.43
psutil==5.9.8
ptyprocess==0.7.0
pure-eval==0.2.2
pycparser==2.22
Pygments==2.17.2
python-dateutil==2.9.0.post0
python-json-logger==2.0.7
PyYAML==6.0.1
pyzmq==25.1.2
qtconsole==5.5.1
QtPy==2.4.1
referencing==0.34.0
requests==2.31.0
rfc3339-validator==0.1.4
rfc3986-validator==0.1.1
rpds-py==0.18.0
Send2Trash==1.8.3
six @ file:///System/Volumes/Data/SWE/Apps/DT/BuildRoots/BuildRoot7/ActiveBuildRoot/Library/Caches/com.apple.xbs/Sources/python3/python3-133.100.1.1/six-1.15.0-py2.py3-none-any.whl
sniffio==1.3.1
soupsieve==2.5
stack-data==0.6.3
terminado==0.18.1
tinycss2==1.2.1
tomli==2.0.1
tornado==6.4
traitlets==5.14.2
types-python-dateutil==2.9.0.20240316
typing_extensions==4.11.0
uri-template==1.3.0
urllib3==2.2.1
wcwidth==0.2.13
webcolors==1.13
webencodings==0.5.1
websocket-client==1.7.0
widgetsnbextension==4.0.10
zipp==3.18.1

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logo: logo-uba.png
footer: |
<body>
Cimbage - IADCOM | Facultad de Ciencias Económicas - UBA |
{{< fa brands github size=1x >}} <a href="https://github.com/karbartolome/workshops">Repositorio</a>
Cimbage - IADCOM | Facultad de Ciencias Económicas - UBA | bit.ly/uba-calibracion
</body>
self-contained: true
embed-resources: true
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## Consideraciones previas
::: {style="font-size: 50%;"}

{{< fa brands github size=1x >}} <a href="https://github.com/karbartolome/workshops" target="_blank">Repositorio</a>

- Se realizará una breve introducción a modelos de aprendizaje automático, pero se recomienda tener algún conocimiento previo para seguir el taller

- Durante el seminario se utilizarán los siguientes paquetes (**python**), con sus respectivas versiones.
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import great_tables
from IPython.display import display, Markdown, Latex
Markdown(f"""
**Procesamiento de datos**:\n
📦 pandas=={pd.__version__}\n
📦 numpy=={np.__version__}\n
\n
**Modelado**:\n
📦 scikit-learn=={sklearn.__version__}\n
\n
**Visualización y tablas:**\n
📦 matplotlib=={matplotlib.__version__}\n
📦 seaborn=={sns.__version__}\n
📦 great_tables=={great_tables.__version__}\n
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\text{Brier Loss} = \frac{1}{N} \sum_{i=1}^{N} (y_i - \hat{y_i})^2
$$


:::
:::
# Comparación de modelos de ML
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## Referencias / Recursos
::: {#refs}
:::


<a href="https://drive.google.com/file/d/1n5ltjBMJ62QCXvHN43DIcQ-mgz_RYICe/view?usp=sharing" target="_blank" style="display: inline-block; text-decoration: none;">
<img src="https://colab.research.google.com/img/colab_favicon_256px.png" alt="Google Colab" style="width: 50px; height: 50px;">
<span style="vertical-align: middle;">Ejemplo en Google Colab</span>
</a>


:::


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## 2024

[CIMBAGE (IADCOM) - Facultad Ciencias Económicas (UBA)](https://karbartolome.github.io/workshops/20240513-cimbage-calibracion/slides)

[Calibración de probabilidades](https://karbartolome.github.io/workshops/20240513-cimbage-calibracion/slides)
CIMBAGE (IADCOM) - Facultad Ciencias Económicas (UBA)


## Configs
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