autopredictor.fit

Module Contents

Functions

fit(X_train, X_test, y_train, y_test[, return_train])

Train and evaluate multiple regression models on the given training and test data.
autopredictor.fit.fit(X_train, X_test, y_train, y_test, return_train=False)[source]

Train and evaluate multiple regression models on the given training and test data.

Parameters:

  • X_train (DataFrame) – Training data features.

  • X_test (DataFrame) – Test data features.

  • y_train (Series) – Training data target values.

  • y_test (Series) – Test data target values.

  • return_train (bool, optional, default=False) – If True, returns scores for training data as well.

Raises:

ValueError – If any of the inputs are empty or None.

Returns:

A tuple containing dictionaries with performance scores for each model and metric. The first dictionary contains scores for test data, and the second for training data.

Return type:

tuple of dict

Examples

>>> X_train = pd.DataFrame({'feature1': [1, 2, 3], 'feature2': [4, 5, 6]})
>>> y_train = pd.Series([10, 20, 30])
>>> X_test = pd.DataFrame({'feature1': [7, 8, 9], 'feature2': [10, 11, 12]})
>>> y_test = pd.Series([40, 50, 60])

>>> scores_train, scores_test = fit(X_train, X_test, y_train, y_test, return_train=True)
>>> print(scores_train['Linear Regression']['Mean Absolute Error'])
0.0
>>> print(scores_test['Linear Regression']['R2 Score'])
-3.0