Class RandomForestRegressor (1.8.0) 

RandomForestRegressor(
    n_estimators: 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,
    tol: float = 0.01,
    enable_global_explain: bool = 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.

Parameters
Name Description
n_estimators Optional[int]

Number of parallel trees constructed during each iteration. Default to 100. Minimum value is 2.
tree_method Optional[str]

Specify which tree method to use. Default to "auto". If this parameter is set to default, XGBoost will choose the most conservative option available. Possible values: "exact", "approx", "hist".
min_child_weight Optional[float]

Minimum sum of instance weight(hessian) needed in a child. Default to 1.
colsample_bytree Optional[float]

Subsample ratio of columns when constructing each tree. Default to 1.0. The value should be between 0 and 1.
colsample_bylevel Optional[float]

Subsample ratio of columns for each level. Default to 1.0. The value should be between 0 and 1.
colsample_bynode Optional[float]

Subsample ratio of columns for each split. Default to 0.8. The value should be between 0 and 1.
gamma Optional[float]

(min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree. Default to 0.0.
max_depth Optional[int]

Maximum tree depth for base learners. Default to 15. The value should be greater than 0 and less than 1.
reg_alpha Optional[float]

L1 regularization term on weights (xgb's alpha). Default to 0.0.
reg_lambda Optional[float]

L2 regularization term on weights (xgb's lambda). Default to 1.0.
tol Optional[float]

Minimum relative loss improvement necessary to continue training. Default to 0.01.
enable_global_explain Optional[bool]

Whether to compute global explanations using explainable AI to evaluate global feature importance to the model. Default to False.
xgboost_version Optional[str]

Specifies the Xgboost version for model training. Default to "0.9". Possible values: "0.9", "1.1".

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
Name Description
X bigframes.dataframe.DataFrame or bigframes.series.Series

Series or DataFrame of shape (n_samples, n_features). Training data.
y bigframes.dataframe.DataFrame or 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.
Returns
Type Description
ForestModel 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 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.

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 The 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 the 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 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

Determine whether to replace if the model already exists. Default to False.
Returns
Type Description
RandomForestRegressor Saved model.