snowflake.ml.modeling.ensemble.GradientBoostingClassifier¶

class snowflake.ml.modeling.ensemble.GradientBoostingClassifier(*, loss='log_loss', learning_rate=0.1, n_estimators=100, subsample=1.0, criterion='friedman_mse', min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_depth=3, min_impurity_decrease=0.0, init=None, random_state=None, max_features=None, verbose=0, max_leaf_nodes=None, warm_start=False, validation_fraction=0.1, n_iter_no_change=None, tol=0.0001, ccp_alpha=0.0, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

Gradient Boosting for classification For more details on this class, see sklearn.ensemble.GradientBoostingClassifier

loss: {‘log_loss’, ‘exponential’}, default=’log_loss’

The loss function to be optimized. ‘log_loss’ refers to binomial and multinomial deviance, the same as used in logistic regression. It is a good choice for classification with probabilistic outputs. For loss ‘exponential’, gradient boosting recovers the AdaBoost algorithm.

learning_rate: float, default=0.1

Learning rate shrinks the contribution of each tree by learning_rate. There is a trade-off between learning_rate and n_estimators. Values must be in the range [0.0, inf).

n_estimators: int, default=100

The number of boosting stages to perform. Gradient boosting is fairly robust to over-fitting so a large number usually results in better performance. Values must be in the range [1, inf).

subsample: float, default=1.0

The fraction of samples to be used for fitting the individual base learners. If smaller than 1.0 this results in Stochastic Gradient Boosting. subsample interacts with the parameter n_estimators. Choosing subsample < 1.0 leads to a reduction of variance and an increase in bias. Values must be in the range (0.0, 1.0].

criterion: {‘friedman_mse’, ‘squared_error’}, default=’friedman_mse’

The function to measure the quality of a split. Supported criteria are ‘friedman_mse’ for the mean squared error with improvement score by Friedman, ‘squared_error’ for mean squared error. The default value of ‘friedman_mse’ is generally the best as it can provide a better approximation in some cases.

min_samples_split: int or float, default=2

The minimum number of samples required to split an internal node:

• If int, values must be in the range [2, inf).

• If float, values must be in the range (0.0, 1.0] and min_samples_split will be ceil(min_samples_split * n_samples).

min_samples_leaf: int or float, default=1

The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.

• If int, values must be in the range [1, inf).

• If float, values must be in the range (0.0, 1.0) and min_samples_leaf will be ceil(min_samples_leaf * n_samples).

min_weight_fraction_leaf: float, default=0.0

The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided. Values must be in the range [0.0, 0.5].

max_depth: int or None, default=3

Maximum depth of the individual regression estimators. The maximum depth limits the number of nodes in the tree. Tune this parameter for best performance; the best value depends on the interaction of the input variables. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. If int, values must be in the range [1, inf).

min_impurity_decrease: float, default=0.0

A node will be split if this split induces a decrease of the impurity greater than or equal to this value. Values must be in the range [0.0, inf).

The weighted impurity decrease equation is the following:

N_t / N * (impurity - N_t_R / N_t * right_impurity
                    - N_t_L / N_t * left_impurity)

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where N is the total number of samples, N_t is the number of samples at the current node, N_t_L is the number of samples in the left child, and N_t_R is the number of samples in the right child.

N, N_t, N_t_R and N_t_L all refer to the weighted sum, if sample_weight is passed.

init: estimator or ‘zero’, default=None

An estimator object that is used to compute the initial predictions. init has to provide fit and predict_proba. If ‘zero’, the initial raw predictions are set to zero. By default, a DummyEstimator predicting the classes priors is used.

random_state: int, RandomState instance or None, default=None

Controls the random seed given to each Tree estimator at each boosting iteration. In addition, it controls the random permutation of the features at each split (see Notes for more details). It also controls the random splitting of the training data to obtain a validation set if n_iter_no_change is not None. Pass an int for reproducible output across multiple function calls. See Glossary.

max_features: {‘sqrt’, ‘log2’}, int or float, default=None

The number of features to consider when looking for the best split:

• If int, values must be in the range [1, inf).

• If float, values must be in the range (0.0, 1.0] and the features considered at each split will be max(1, int(max_features * n_features_in_)).

• If ‘sqrt’, then max_features=sqrt(n_features).

• If ‘log2’, then max_features=log2(n_features).

• If None, then max_features=n_features.

Choosing max_features < n_features leads to a reduction of variance and an increase in bias.

Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.

verbose: int, default=0

Enable verbose output. If 1 then it prints progress and performance once in a while (the more trees the lower the frequency). If greater than 1 then it prints progress and performance for every tree. Values must be in the range [0, inf).

max_leaf_nodes: int, default=None

Grow trees with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. Values must be in the range [2, inf). If None, then unlimited number of leaf nodes.

warm_start: bool, default=False

When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just erase the previous solution. See the Glossary.

validation_fraction: float, default=0.1

The proportion of training data to set aside as validation set for early stopping. Values must be in the range (0.0, 1.0). Only used if n_iter_no_change is set to an integer.

n_iter_no_change: int, default=None

n_iter_no_change is used to decide if early stopping will be used to terminate training when validation score is not improving. By default it is set to None to disable early stopping. If set to a number, it will set aside validation_fraction size of the training data as validation and terminate training when validation score is not improving in all of the previous n_iter_no_change numbers of iterations. The split is stratified. Values must be in the range [1, inf).

tol: float, default=1e-4

Tolerance for the early stopping. When the loss is not improving by at least tol for n_iter_no_change iterations (if set to a number), the training stops. Values must be in the range [0.0, inf).

ccp_alpha: non-negative float, default=0.0

Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than ccp_alpha will be chosen. By default, no pruning is performed. Values must be in the range [0.0, inf). See minimal_cost_complexity_pruning for details.

input_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.

label_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).

output_cols: Optional[Union[str, List[str]]]

A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.

sample_weight_col: Optional[str]

A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.

drop_input_cols: Optional[bool], default=False

If set, the response of predict(), transform() methods will not contain input columns.

Methods

decision_function(dataset[, output_cols_prefix])	Compute the decision function of X For more details on this function, see sklearn.ensemble.GradientBoostingClassifier.decision_function
fit(dataset)	Fit the gradient boosting model For more details on this function, see sklearn.ensemble.GradientBoostingClassifier.fit
predict(dataset)	Predict class for X For more details on this function, see sklearn.ensemble.GradientBoostingClassifier.predict
predict_log_proba(dataset[, output_cols_prefix])	Predict class probabilities for X For more details on this function, see sklearn.ensemble.GradientBoostingClassifier.predict_proba
predict_proba(dataset[, output_cols_prefix])	Predict class probabilities for X For more details on this function, see sklearn.ensemble.GradientBoostingClassifier.predict_proba
score(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.ensemble.GradientBoostingClassifier.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.ensemble.GradientBoostingClassifier object.

Attributes

model_signatures

Returns model signature of current class.