Note
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Regression Systematic Error#
This notebook provides an overview for using and understanding the Regression Systematic Error check.
This check is deprecated and will be removed in future versions, please use the Regression Error Distribution check instead.
Structure:
What is the Regression Systematic Error check?#
The RegressionSystematicError check looks for a systematic error in model predictions.
If the errors distribution is non-zero mean, it indicates the presence of a systematic error.
Imports#
from sklearn.datasets import load_diabetes
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import train_test_split
from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import RegressionSystematicError
Generate data & model#
diabetes_df = load_diabetes(return_X_y=False, as_frame=True).frame
train_df, test_df = train_test_split(diabetes_df, test_size=0.33, random_state=42)
train_df['target'] = train_df['target'] + 150
train = Dataset(train_df, label='target', cat_features=['sex'])
test = Dataset(test_df, label='target', cat_features=['sex'])
clf = GradientBoostingRegressor(random_state=0)
_ = clf.fit(train.data[train.features], train.data[train.label_name])
Run the check#
check = RegressionSystematicError()
check.run(test, clf)
/home/runner/work/deepchecks/deepchecks/docs/source/checks/tabular/model_evaluation/plot_regression_systematic_error.py:55: DeprecationWarning:
RegressionSystematicError check is deprecated and will be removed in future version, please use RegressionErrorDistribution check instead.
Total running time of the script: ( 0 minutes 0.135 seconds)