Accuracy: Regression

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")

Accuracy table

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

Plot residual with respect to the feature hr

exp.model_diagnose(model="XGB2", show="accuracy_residual", show_feature="hr",
                   use_test=False, original_scale=True, figsize=(5, 4))

Plot residual with respect to the feature season

exp.model_diagnose(model="XGB2", show="accuracy_residual", show_feature="season",
                   use_test=False, original_scale=True, figsize=(5, 4))

Plot residual with respect to the target feature

exp.model_diagnose(model="XGB2", show="accuracy_residual", show_feature="cnt",
                   use_test=False, figsize=(5, 4))

Plot residual with respect to the model prediction

exp.model_diagnose(model="XGB2", show="accuracy_residual", show_feature="cnt_predict",
                   use_test=False, figsize=(5, 4))

Estimated memory usage: 20 MB