8.8.1. sklearn.feature_selection.SelectPercentile¶
- class sklearn.feature_selection.SelectPercentile(score_func, percentile=10)¶
Filter: Select the best percentile of the p_values
Parameters : score_func: callable :
function taking two arrays X and y, and returning 2 arrays: both scores and pvalues
percentile: int, optional :
percent of features to keep
Methods
fit(X, y) Evaluate the function fit_transform(X[, y]) Fit to data, then transform it get_support([indices]) Return a mask, or list, of the features/indices selected. inverse_transform(X) Transform a new matrix using the selected features set_params(**params) Set the parameters of the estimator. transform(X) Transform a new matrix using the selected features - __init__(score_func, percentile=10)¶
- fit(X, y)¶
Evaluate the function
- fit_transform(X, y=None, **fit_params)¶
Fit to data, then transform it
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
Parameters : X : numpy array of shape [n_samples, n_features]
Training set.
y : numpy array of shape [n_samples]
Target values.
Returns : X_new : numpy array of shape [n_samples, n_features_new]
Transformed array.
Notes
This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.
- get_support(indices=False)¶
Return a mask, or list, of the features/indices selected.
- inverse_transform(X)¶
Transform a new matrix using the selected features
- set_params(**params)¶
Set the parameters of the estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Returns : self :
- transform(X)¶
Transform a new matrix using the selected features