EBM Regression (Bike Sharing)

Experiment initialization and data preparation

from piml import Experiment
from piml.models import ExplainableBoostingRegressor

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=ExplainableBoostingRegressor(interactions=10), name="EBM")

Evaluate predictive performance

exp.model_diagnose(model="EBM", show="accuracy_table")

          MSE     MAE       R2

Train  0.0072  0.0589   0.7918
Test   0.0078  0.0615   0.7779
Gap    0.0006  0.0025  -0.0140

Global effect plot for hr

exp.model_interpret(model="EBM", show="global_effect_plot", uni_feature="hr",
                    original_scale=True, figsize=(5, 4))

Global effect plot for season

exp.model_interpret(model="EBM", show="global_effect_plot", uni_feature="season", figsize=(5, 4))

Global effect plot for hr and season

exp.model_interpret(model="EBM", show="global_effect_plot", bi_features=["hr", "season"],
                    original_scale=True, figsize=(5, 4))

Effect importance

exp.model_interpret(model="EBM", show="global_ei", figsize=(5, 4))

Feature importance

exp.model_interpret(model="EBM", show="global_fi", figsize=(5, 4))

Local interpretation by effect

exp.model_interpret(model="EBM", show="local_ei", sample_id=0, original_scale=True, figsize=(5, 4))

Local interpretation by feature

exp.model_interpret(model="EBM", show="local_fi", sample_id=0, original_scale=True, figsize=(5, 4))

Estimated memory usage: 14 MB