fast-explain 0.0.83

Fit Fast, Explain Fast

# FastExplain > Fit Fast, Explain Fast

## Installing ` pip install fast-explain ` ## Clean Data, Fit ML Models and Explore Results all in one line. FastExplain provides an out-of-the-box tool for analysts to quickly explore data, train and interpret models, with flexibility to fine-tune if needed. - Automated cleaning and fitting of machine learning models with hyperparameter search - Aesthetic display of explanatory methods ready for reporting - Connected interface for all data, models and related explanatory methods

## Quickstart

[](https://colab.research.google.com/github/felixzhu17/FastExplain/blob/main/demos/FastExplain%20Titanic%20Quickstart.ipynb)

### Automated Cleaning and Fitting ` python from FastExplain import * df = load_titanic_data() classification = model_data(df, dep_var="Survived", model="ebm") ` ### Aesthetic Display ` python feature_correlation(classification.data.df) ` <img alt=”Feature Correlation” src=”images/feature_correlation.png”>

` python plot_one_way_analysis(classification.data.df, "Age", "Survived", filter = "Sex == 1") ` <img alt=”One Way” src=”images/one_way.png”>

` python plot_ebm_explain(classification.m, classification.data.df, "Age") ` <img alt=”EBM” src=”images/ebm.png”>

` python plot_ale(classification.m, classification.data.xs, "Age", filter = "Sex == 1", dep_name = "Survived") ` <img alt=”ALE” src=”images/ALE.png”>

` python classification_1 = model_data(df, dep_var="Survived", model="rf", hypertune=True, cont_names=['Age'], cat_names = [], hypertune=True) models = [classification.m, classification_1.m] data = [classification.data.xs, classification_1.data.xs] plot_ale(models, data, 'Age', dep_name = "Survived") ` <img alt=”multi_ALE” src=”images/multi_ALE.png”>

### Connected Interface ` python classification_1.plot_one_way_analysis("Age", filter = "Sex == 1") classification_1.plot_ale("Age", filter = "Sex == 1") `

` python classification_1.shap_dependence_plot("Age", filter = "Sex == 1") ` <img alt=”SHAP” src=”images/shap.png”>

` python classification_1.error # {'auc': {'model': {'train': 0.9934332941166654, # 'val': 0.8421607378129118, # 'overall': 0.9665739941840028}}, # 'cross_entropy': {'model': {'train': 0.19279692001978943, # 'val': 0.4600233891109683, # 'overall': 0.24648214781700722}}} `

## Models Supported - Random Forest - XGBoost - Explainable Boosting Machine - ANY Model Class with fit and predict attributes

` python pip install lightgbm `

` python from lightgbm import LGBMClassifier custom_model = model_data(df, 'Survived', model=LGBMClassifier) custom_model.plot_ale("Age") custom_model.shap_dependence_plot("Age") `

## Exploratory Methods Supported: - One-way Analysis - Two-way Analysis - Feature Importance Plots - ALE Plots - Explainable Boosting Methods - SHAP Values - Partial Dependence Plots - Sensitivity Analysis