SHapley Additive exPlanations

Experiment initialization and data preparation

from piml import Experiment
from piml.models import XGB2Regressor

exp = Experiment()
exp.data_loader(data="BikeSharing", silent=True)
exp.data_summary(feature_exclude=["yr", "mnth", "temp"], silent=True)
exp.data_prepare(target="cnt", task_type="regression", silent=True)

Train Model

exp.model_train(model=XGB2Regressor(), name="XGB2")

SHAP Waterfall plot

exp.model_explain(model="XGB2", show="shap_waterfall", sample_id=0, figsize=(5, 4))

SHAP feature importance

exp.model_explain(model="XGB2", show="shap_fi", sample_size=100, figsize=(5, 4))

SHAP summary plot

exp.model_explain(model="XGB2", show="shap_summary", sample_size=100, figsize=(5, 4))

SHAP scatter plot

exp.model_explain(model="XGB2", show="shap_scatter", uni_feature="hr",
                  sample_size=100, figsize=(5, 4))

Estimated memory usage: 33 MB