snowflake.ml.modeling.svm.LinearSVR¶

class snowflake.ml.modeling.svm.LinearSVR(*, epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive', fit_intercept=True, intercept_scaling=1.0, dual='warn', verbose=0, random_state=None, max_iter=1000, 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

Linear Support Vector Regression For more details on this class, see sklearn.svm.LinearSVR

epsilon: float, default=0.0

Epsilon parameter in the epsilon-insensitive loss function. Note that the value of this parameter depends on the scale of the target variable y. If unsure, set epsilon=0.

tol: float, default=1e-4

Tolerance for stopping criteria.

C: float, default=1.0

Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive.

loss: {‘epsilon_insensitive’, ‘squared_epsilon_insensitive’}, default=’epsilon_insensitive’

Specifies the loss function. The epsilon-insensitive loss (standard SVR) is the L1 loss, while the squared epsilon-insensitive loss (‘squared_epsilon_insensitive’) is the L2 loss.

fit_intercept: bool, default=True

Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).

intercept_scaling: float, default=1.0

When self.fit_intercept is True, instance vector x becomes [x, self.intercept_scaling], i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended to the instance vector. The intercept becomes intercept_scaling * synthetic feature weight Note! the synthetic feature weight is subject to l1/l2 regularization as all other features. To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept) intercept_scaling has to be increased.

dual: “auto” or bool, default=True

Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features. dual=”auto” will choose the value of the parameter automatically, based on the values of n_samples, n_features and loss. If n_samples < n_features and optmizer supports chosen loss, then dual will be set to True, otherwise it will be set to False.

verbose: int, default=0

Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in liblinear that, if enabled, may not work properly in a multithreaded context.

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

Controls the pseudo random number generation for shuffling the data. Pass an int for reproducible output across multiple function calls. See Glossary.

max_iter: int, default=1000

The maximum number of iterations to be run.

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.svm.LinearSVR.fit
predict(dataset)	Predict using the linear model For more details on this function, see sklearn.svm.LinearSVR.predict
score(dataset)	Return the coefficient of determination of the prediction For more details on this function, see sklearn.svm.LinearSVR.score
set_input_cols(input_cols)	Input columns setter.
to_sklearn()	Get sklearn.svm.LinearSVR object.

Attributes

model_signatures

Returns model signature of current class.