evalml.pipelines.components.SVMRegressor

class evalml.pipelines.components.SVMRegressor(C=1.0, kernel='rbf', gamma='scale', random_state=0, **kwargs)[source]

Support Vector Machine Regressor.

name = 'SVM Regressor'
model_family = 'svm'
supported_problem_types = [<ProblemTypes.REGRESSION: 'regression'>, <ProblemTypes.TIME_SERIES_REGRESSION: 'time series regression'>]
hyperparameter_ranges = {'C': Real(low=0, high=10, prior='uniform', transform='identity'), 'gamma': ['scale', 'auto'], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']}
default_parameters = {'C': 1.0, 'gamma': 'scale', 'kernel': 'rbf'}
predict_uses_y = False

Instance attributes

feature_importance

Feature importance only works with linear kernels.

needs_fitting

parameters

Returns the parameters which were used to initialize the component

Methods:

__init__

Initialize self.

clone

Constructs a new component with the same parameters and random state.

describe

Describe a component and its parameters

fit

Fits component to data

load

Loads component at file path

predict

Make predictions using selected features.

predict_proba

Make probability estimates for labels.

save

Saves component at file path

Class Inheritance

Inheritance diagram of SVMRegressor