Fit Fast, Explain Fast
pip install fast-explain
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
from FastExplain import *
df = load_titanic_data()
classification = model_data(df, dep_var="Survived", model="ebm")feature_correlation(classification.data.df)
plot_one_way_analysis(classification.data.df, "Age", "Survived", filter = "Sex == 1")
plot_ebm_explain(classification.m, classification.data.df, "Age")
plot_ale(classification.m, classification.data.xs, "Age", filter = "Sex == 1", dep_name = "Survived")
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")
classification_1.plot_one_way_analysis("Age", filter = "Sex == 1")
classification_1.plot_ale("Age", filter = "Sex == 1")classification_1.shap_dependence_plot("Age", filter = "Sex == 1")
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}}}- Random Forest
- XGBoost
- Explainable Boosting Machine
- ANY Model Class with fit and predict attributes
pip install lightgbmfrom lightgbm import LGBMClassifier
custom_model = model_data(df, 'Survived', model=LGBMClassifier)
custom_model.plot_ale("Age")
custom_model.shap_dependence_plot("Age")- One-way Analysis
- Two-way Analysis
- Feature Importance Plots
- ALE Plots
- Explainable Boosting Methods
- SHAP Values
- Partial Dependence Plots
- Sensitivity Analysis