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, 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

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.

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

fit(dataset)	Fit the model according to the given training data For more details on this function, see sklearn.linear_model.HuberRegressor.fit
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_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.linear_model.HuberRegressor object.

Attributes

model_signatures

Returns model signature of current class.