bigframes.ml.linear_model.LogisticRegression.fit#
- LogisticRegression.fit(X: DataFrame | Series | DataFrame | Series, y: DataFrame | Series | DataFrame | Series, X_eval: DataFrame | Series | DataFrame | Series | None = None, y_eval: DataFrame | Series | DataFrame | Series | None = None) _T#
Fit the model according to the given training data.
- Parameters:
X (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series) – Series or DataFrame of shape (n_samples, n_features). Training vector, where n_samples is the number of samples and n_features is the number of features.
y (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series) – DataFrame of shape (n_samples,). Target vector relative to X.
X_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series) – Series or DataFrame of shape (n_samples, n_features). Evaluation vector, where n_samples is the number of samples and n_features is the number of features.
y_eval (bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series) – DataFrame of shape (n_samples,). Target vector relative to X_eval.
- Returns:
Fitted estimator.
- Return type: