Class RandomForestClassifier (0.12.0)

RandomForestClassifier(
    num_parallel_tree: int = 100,
    tree_method: typing.Literal["auto", "exact", "approx", "hist"] = "auto",
    min_tree_child_weight: int = 1,
    colsample_bytree: float = 1.0,
    colsample_bylevel: float = 1.0,
    colsample_bynode: float = 0.8,
    gamma: float = 0.0,
    max_depth: int = 15,
    subsample: float = 0.8,
    reg_alpha: float = 0.0,
    reg_lambda: float = 1.0,
    early_stop=True,
    min_rel_progress: float = 0.01,
    enable_global_explain=False,
    xgboost_version: typing.Literal["0.9", "1.1"] = "0.9",
)

A random forest classifier.

A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.

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

Build a forest of trees from the training set (X, y).

Parameters
NameDescription
X typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Series or DataFrame of shape (n_samples, n_features). Training data.

y typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]

Series or DataFrame of shape (n_samples,) or (n_samples, n_targets). Target values. Will be cast to X's dtype if necessary.

get_params

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

Get parameters for this estimator.

Parameter
NameDescription
deep bool, default True

Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
TypeDescription
DictionaryA dictionary of parameter names mapped to their values.

predict

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

Predict regression target for X.

The predicted regression target of an input sample is computed as the mean predicted regression targets of the trees in the forest.

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
NameDescription
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],
)

Calculate evaluation metrics of the model.

Parameters
NameDescription
X bigframes.dataframe.DataFrame or bigframes.series.Series

A BigQuery DataFrame as evaluation data.

y bigframes.dataframe.DataFrame or bigframes.series.Series

A BigQuery DataFrame as evaluation labels.

Returns
TypeDescription
bigframes.dataframe.DataFrameThe DataFrame as evaluation result.

to_gbq

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

Save the model to BigQuery.

Parameters
NameDescription
model_name str

the name of the model.

replace bool, default False

whether to replace if the model already exists. Default to False.

Returns
TypeDescription
RandomForestClassifiersaved model.