fast-explain 0.0.6

Fit Fast, Explain Fast

# FastExplain > Fit Fast, Explain Fast

## Installing ` pip install fast-explain ` ## About FastExplain FastExplain provides an out-of-the-box methodology for analysts to quickly explore data, with flexibility to fine-tune if needed. - Automated fitting of machine learning models with hyperparameter search - Aesthetic display of explanatory methods ready for reporting - Connected interface for all models and related explanatory methods

## Quickstart ### Automated Fitting ` python from FastExplain import model_data from FastExplain.datasets import load_titanic_data df = load_titanic_data() classification = model_data(df, 'Survived', hypertune=True) ` ### Aesthetic Display ` python from FastExplain.explain import plot_one_way_analysis, plot_ale ` ` python plot_one_way_analysis(classification.data.df, "Age", "Survived", filter = "Sex == 1") ` <img alt=”One Way” src=”images/one_way.png”>

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

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

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

` python classification.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

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