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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],
) -> bigframes.ml.base._T
Build a forest of trees from the training set (X, y).
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 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 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. |