Class RandomForestRegressor (0.3.0)

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

A random forest regressor.

A random forest is a meta estimator that fits a number of classifying decision trees 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],
    transforms: typing.Optional[typing.List[str]] = None,
) -> bigframes.ml.ensemble.RandomForestRegressor

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

Parameter
Name	Description
`transforms`	`Optional[List[str]], default None` Do not use. Internal param to be deprecated. Use bigframes.ml.pipeline instead.

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 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

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],
)

Calculate evaluation metrics of the model.

Parameters
Name	Description
`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
Type	Description
`bigframes.dataframe.DataFrame`	The DataFrame as evaluation result.

to_gbq

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

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
`RandomForestRegressor`	saved model.