svm_classifier¶

Module Contents¶

Classes Summary¶

SVMClassifier

Support Vector Machine Classifier.

Contents¶

class evalml.pipelines.components.estimators.classifiers.svm_classifier.SVMClassifier(C=1.0, kernel='rbf', gamma='scale', probability=True, random_seed=0, **kwargs)[source]¶

Support Vector Machine Classifier.

Parameters

C (float) – The regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty. Defaults to 1.0.
kernel ({"linear", "poly", "rbf", "sigmoid", "precomputed"}) – Specifies the kernel type to be used in the algorithm. Defaults to “rbf”.
gamma ({"scale", "auto"} or float) – Kernel coefficient for “rbf”, “poly” and “sigmoid”. Defaults to “scale”. - If gamma=’scale’ (default) is passed then it uses 1 / (n_features * X.var()) as value of gamma - if “auto”, uses 1 / n_features
probability (boolean) – Whether to enable probability estimates. Defaults to True.
random_seed (int) – Seed for the random number generator. Defaults to 0.

Attributes

hyperparameter_ranges	{ “C”: Real(0, 10), “kernel”: [“linear”, “poly”, “rbf”, “sigmoid”, “precomputed”], “gamma”: [“scale”, “auto”],}
model_family	ModelFamily.SVM
modifies_features	True
modifies_target	False
name	SVM Classifier
predict_uses_y	False
supported_problem_types	[ ProblemTypes.BINARY, ProblemTypes.MULTICLASS, ProblemTypes.TIME_SERIES_BINARY, ProblemTypes.TIME_SERIES_MULTICLASS,]

Methods

`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters
`feature_importance`	Feature importance only works with linear kernels.
`fit`	Fits component to data
`load`	Loads component at file path
`needs_fitting`	Returns boolean determining if component needs fitting before
`parameters`	Returns the parameters which were used to initialize the component
`predict`	Make predictions using selected features.
`predict_proba`	Make probability estimates for labels.
`save`	Saves component at file path

clone(self)¶

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

Returns: A new instance of this component with identical parameters and random state.

default_parameters(cls)¶

Returns the default parameters for this component.

Our convention is that Component.default_parameters == Component().parameters.

Returns: default parameters for this component.
Return type: dict

describe(self, print_name=False, return_dict=False)¶

Describe a component and its parameters

Parameters

print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}

Returns

prints and returns dictionary

Return type

None or dict

property feature_importance(self)¶: Feature importance only works with linear kernels. If the kernel isn’t linear, we return a numpy array of zeros

fit(self, X, y=None)¶

Fits component to data

Parameters

X (list, pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]
y (list, pd.Series, np.ndarray, optional) – The target training data of length [n_samples]

Returns

self

static load(file_path)¶

Loads component at file path

Parameters: file_path (str) – Location to load file
Returns: ComponentBase object

needs_fitting(self)¶: Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances. This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.

property parameters(self)¶: Returns the parameters which were used to initialize the component

predict(self, X)¶

Make predictions using selected features.

Parameters: X (pd.DataFrame, np.ndarray) – Data of shape [n_samples, n_features]
Returns: Predicted values
Return type: pd.Series

predict_proba(self, X)¶

Make probability estimates for labels.

Parameters: X (pd.DataFrame, or np.ndarray) – Features
Returns: Probability estimates
Return type: pd.Series

save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)¶

Saves component at file path

Parameters

file_path (str) – Location to save file
pickle_protocol (int) – The pickle data stream format.

Returns

None

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