class sklearn.linear_model.OrthogonalMatchingPursuitCV(copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=None, n_jobs=1, verbose=False)[source]

Cross-validated Orthogonal Matching Pursuit model (OMP)

Read more in the User Guide.


copy : bool, optional

Whether the design matrix X must be copied by the algorithm. A false value is only helpful if X is already Fortran-ordered, otherwise a copy is made anyway.

fit_intercept : boolean, optional

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

normalize : boolean, optional, default True

This parameter is ignored when fit_intercept is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use sklearn.preprocessing.StandardScaler before calling fit on an estimator with normalize=False.

max_iter : integer, optional

Maximum numbers of iterations to perform, therefore maximum features to include. 10% of n_features but at least 5 if available.

cv : int, cross-validation generator or an iterable, optional

Determines the cross-validation splitting strategy. Possible inputs for cv are:

  • None, to use the default 3-fold cross-validation,
  • integer, to specify the number of folds.
  • An object to be used as a cross-validation generator.
  • An iterable yielding train/test splits.

For integer/None inputs, KFold is used.

Refer User Guide for the various cross-validation strategies that can be used here.

n_jobs : integer, optional

Number of CPUs to use during the cross validation. If -1, use all the CPUs

verbose : boolean or integer, optional

Sets the verbosity amount


intercept_ (float or array, shape (n_targets,)) Independent term in decision function.
coef_ (array, shape (n_features,) or (n_targets, n_features)) Parameter vector (w in the problem formulation).
n_nonzero_coefs_ (int) Estimated number of non-zero coefficients giving the best mean squared error over the cross-validation folds.
n_iter_ (int or array-like) Number of active features across every target for the model refit with the best hyperparameters got by cross-validating across all folds.


fit(X, y) Fit the model using X, y as training data.
get_params([deep]) Get parameters for this estimator.
predict(X) Predict using the linear model
score(X, y[, sample_weight]) Returns the coefficient of determination R^2 of the prediction.
set_params(**params) Set the parameters of this estimator.
__init__(copy=True, fit_intercept=True, normalize=True, max_iter=None, cv=None, n_jobs=1, verbose=False)[source]
fit(X, y)[source]

Fit the model using X, y as training data.


X : array-like, shape [n_samples, n_features]

Training data.

y : array-like, shape [n_samples]

Target values. Will be cast to X’s dtype if necessary


self : object

returns an instance of self.


Get parameters for this estimator.


deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.


params : mapping of string to any

Parameter names mapped to their values.


Predict using the linear model


X : {array-like, sparse matrix}, shape = (n_samples, n_features)



C : array, shape = (n_samples,)

Returns predicted values.

score(X, y, sample_weight=None)[source]

Returns the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.


X : array-like, shape = (n_samples, n_features)

Test samples.

y : array-like, shape = (n_samples) or (n_samples, n_outputs)

True values for X.

sample_weight : array-like, shape = [n_samples], optional

Sample weights.


score : float

R^2 of self.predict(X) wrt. y.


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self Examples using sklearn.linear_model.OrthogonalMatchingPursuitCV