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LogisticRegression(
fit_intercept: bool = True,
class_weights: typing.Optional[
typing.Union[typing.Literal["balanced"], typing.Dict[str, float]]
] = None,
)
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. |
class_weights |
dict or 'balanced', default None
Default None. Weights associated with classes in the form |
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 |
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 |
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, n_input_columns + n_prediction_columns). Returns predicted values. |
register
register(vertex_ai_model_id: typing.Optional[str] = None) -> bigframes.ml.base._T
Register the model to Vertex AI.
After register, go to Google Cloud Console (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 |
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. |