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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))
Total running time of the script: ( 0 minutes 47.407 seconds)
Estimated memory usage: 20 MB