svm_regressor¶
Module Contents¶
Classes Summary¶
Support Vector Machine Regressor. |
Contents¶
-
class
evalml.pipelines.components.estimators.regressors.svm_regressor.SVMRegressor(C=1.0, kernel='rbf', gamma='scale', random_seed=0, **kwargs)[source]¶ Support Vector Machine Regressor.
- 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
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 Regressor
predict_uses_y
False
supported_problem_types
[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,]
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters
Feature importance only works with linear kernels.
Fits component to data
Loads component at file path
Returns boolean determining if component needs fitting before
Returns the parameters which were used to initialize the component
Make predictions using selected features.
Make probability estimates for labels.
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