XGB-2 Regression (Bike Sharing)

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")

Evaluate predictive performance

exp.model_diagnose(model="XGB2", show="accuracy_table")

          MSE     MAE       R2

Train  0.0090  0.0669   0.7382
Test   0.0095  0.0688   0.7287
Gap    0.0005  0.0019  -0.0095

Global effect plot for season

exp.model_interpret(model="XGB2", show="global_effect_plot", uni_feature="season", figsize=(5, 4))

Global effect plot for hr

exp.model_interpret(model="XGB2", show="global_effect_plot", uni_feature="atemp", original_scale=True, figsize=(5, 4))

Global effect plot for hr and season

exp.model_interpret(model="XGB2", show="global_effect_plot", bi_features=["hr", "season"],
                    sliced_line=False, original_scale=True, figsize=(5, 4))

Effect importance

exp.model_interpret(model="XGB2", show="global_ei", figsize=(5, 4))

Feature importance

exp.model_interpret(model="XGB2", show="global_fi", figsize=(5, 4))

Local interpretation by effect

exp.model_interpret(model="XGB2", show="local_ei", sample_id=0, original_scale=True, figsize=(5, 4))

Local interpretation by feature

exp.model_interpret(model="XGB2", show="local_fi", sample_id=0, original_scale=True, figsize=(5, 4))

Estimated memory usage: 31 MB