Class LogisticRegression (0.4.0)

LogisticRegression(fit_intercept: bool = True, auto_class_weights: bool = False)

Logistic Regression (aka logit, MaxEnt) classifier.

Parameters
Name	Description
`fit_intercept`	`default True` Default True. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.
`auto_class_weights`	`default False` Default False. If True, balance class labels using weights for each class in inverse proportion to the frequency of that class.

Methods

repr

__repr__()

Print the estimator's constructor with all non-default parameter values

fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.ml.base._T

Fit the model according to the given training data.

Parameters
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.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` DataFrame of shape (n_samples,). Target vector relative to X.

Returns
Type	Description
`LogisticRegression`	Fitted Estimator.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
`deep`	`bool, default True` Default `True`. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
`Dictionary`	A dictionary of parameter names mapped to their values.

predict

predict(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Predict class labels for samples in X.

Parameter
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series` Series or DataFrame of shape (n_samples, n_features). The data matrix for which we want to get the predictions.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	DataFrame of shape (n_samples,), containing the class labels for each sample.

register

register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T

After register, go to https://console.cloud.google.com/vertex-ai/models to manage the model registries. Refer to https://cloud.google.com/vertex-ai/docs/model-registry/introduction for more options.

Parameter
Name	Description
`vertex_ai_model_id`	`Optional[str], default None` optional string id as model id in Vertex. If not set, will by default to 'bigframes_{bq_model_id}'. Vertex Ai model id will be truncated to 63 characters due to its limitation.

score

score(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
) -> bigframes.dataframe.DataFrame

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters
Name	Description
`X`	`bigframes.dataframe.DataFrame or bigframes.series.Series` DataFrame of shape (n_samples, n_features). Test samples.
`y`	`bigframes.dataframe.DataFrame or bigframes.series.Series` DataFrame of shape (n_samples,) or (n_samples, n_outputs). True labels for `X`.

Returns
Type	Description
`bigframes.dataframe.DataFrame`	A DataFrame of the evaluation result.

to_gbq

to_gbq(
    model_name: str, replace: bool = False
) -> bigframes.ml.linear_model.LogisticRegression

Save the model to BigQuery.

Parameters
Name	Description
`model_name`	`str` the name of the model.
`replace`	`bool, default False` whether to replace if the model already exists. Default to False.

Returns
Type	Description
`LogisticRegression`	saved model.