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XGBClassifier(
n_estimators: int = 1,
*,
booster: typing.Literal["gbtree", "dart"] = "gbtree",
dart_normalized_type: typing.Literal["tree", "forest"] = "tree",
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 = 1.0,
gamma: float = 0.0,
max_depth: int = 6,
subsample: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 1.0,
learning_rate: float = 0.3,
max_iterations: int = 20,
tol: float = 0.01,
enable_global_explain: bool = False,
xgboost_version: typing.Literal["0.9", "1.1"] = "0.9"
)
XGBoost classifier model.
Parameters |
|
---|---|
Name | Description |
n_estimators |
Optional[int]
Number of parallel trees constructed during each iteration. Default to 1. |
booster |
Optional[str]
Specify which booster to use: gbtree or dart. Default to "gbtree". |
dart_normalized_type |
Optional[str]
Type of normalization algorithm for DART booster. Possible values: "TREE", "FOREST". Default to "TREE". |
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. |
colsample_bylevel |
Optional[float]
Subsample ratio of columns for each level. Default to 1.0. |
colsample_bynode |
Optional[float]
Subsample ratio of columns for each split. Default to 1.0. |
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 6. |
subsample |
Optional[float]
Subsample ratio of the training instance. Default to 1.0. |
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. |
learning_rate |
Optional[float]
Boosting learning rate (xgb's "eta"). Default to 0.3. |
max_iterations |
Optional[int]
Maximum number of rounds for boosting. Default to 20. |
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
Fit gradient boosting model.
Note that calling fit()
multiple times will cause the model object to be
re-fit from scratch. To resume training from a previous checkpoint, explicitly
pass xgb_model
argument.
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
DataFrame of shape (n_samples,) or (n_samples, n_targets). Target values. Will be cast to X's dtype if necessary. |
Returns | |
---|---|
Type | Description |
XGBModel |
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 using the XGB model.
Parameter | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
Series or DataFrame of shape (n_samples, n_features). Samples. |
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 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],
)
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 that each label set be correctly predicted for each sample.
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.ensemble.XGBClassifier
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 |
XGBClassifier |
Saved model. |