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XGBClassifier
(
num_parallel_tree
:
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
,
early_stop
:
bool
=
True
,
learning_rate
:
float
=
0.3
,
max_iterations
:
int
=
20
,
min_rel_progress
:
float
=
0.01
,
enable_global_explain
:
bool
=
False
,
xgboost_version
:
typing
.
Literal
[
"0.9"
,
"1.1"
]
=
"0.9"
,
)
XGBoost classifier model.
Parameters
num_parallel_tree
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.
early_stop
Optional[bool]
Whether training should stop after the first iteration. Default to True.
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.
min_rel_progress
Optional[float]
Minimum relative loss improvement necessary to continue training when early_stop is set to True. 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.
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.
XGBModel
get_params
get_params
(
deep
:
bool
=
True
)
-
> typing
.
Dict
[
str
,
typing
.
Any
]
Get parameters for this estimator.
deep
bool, default True
Default True
. If True, will return the parameters for this estimator and contained subobjects that are estimators.
Dictionary
predict
predict
(
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
]
)
-
> bigframes
.
dataframe
.
DataFrame
Predict using the XGB model.
X
bigframes.dataframe.DataFrame
or bigframes.series.Series
Series or DataFrame of shape (n_samples, n_features). Samples.
DataFrame of shape (n_samples,)
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.
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
],
)
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 for each sample that each label set be correctly predicted.
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 X
.
to_gbq
to_gbq
(
model_name
:
str
,
replace
:
bool
=
False
)
-
> bigframes
.
ml
.
ensemble
.
XGBClassifier
Save the model to BigQuery.
model_name
str
the name of the model.
replace
bool, default False
whether to replace if the model already exists. Default to False.
XGBClassifier
