evalml.pipelines.components.SVMRegressor

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

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

evalml.pipelines.components.LightGBMRegressor.save evalml.pipelines.components.SVMRegressor.__init__