snowflake.ml.modeling.linear_model.HuberRegressor¶

class snowflake.ml.modeling.linear_model.HuberRegressor(*, epsilon=1.35, max_iter=100, alpha=0.0001, warm_start=False, fit_intercept=True, tol=1e-05, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

L2-regularized linear regression model that is robust to outliers For more details on this class, see sklearn.linear_model.HuberRegressor

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, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.

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

A string or list of strings representing column names that contain labels. Label columns must be specified with this parameter during initialization or with the set_label_cols method before fitting.

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 must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.

sample_weight_col: Optional[str]

A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.

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

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.

drop_input_cols: Optional[bool], default=False

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

epsilon: float, default=1.35

The parameter epsilon controls the number of samples that should be classified as outliers. The smaller the epsilon, the more robust it is to outliers. Epsilon must be in the range [1, inf).

max_iter: int, default=100

Maximum number of iterations that scipy.optimize.minimize(method="L-BFGS-B") should run for.

alpha: float, default=0.0001

Strength of the squared L2 regularization. Note that the penalty is equal to alpha * ||w||^2. Must be in the range [0, inf).

warm_start: bool, default=False

This is useful if the stored attributes of a previously used model has to be reused. If set to False, then the coefficients will be rewritten for every call to fit. See the Glossary.

fit_intercept: bool, default=True

Whether or not to fit the intercept. This can be set to False if the data is already centered around the origin.

tol: float, default=1e-05

The iteration will stop when max{|proj g_i | i = 1, ..., n} <= tol where pg_i is the i-th component of the projected gradient.

Methods

fit(dataset)	Fit the model according to the given training data For more details on this function, see sklearn.linear_model.HuberRegressor.fit
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
predict(dataset)	Predict using the linear model For more details on this function, see sklearn.linear_model.HuberRegressor.predict
score(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.HuberRegressor.score
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_sklearn()	Get sklearn.linear_model.HuberRegressor object.

Attributes

model_signatures

Returns model signature of current class.