sklearn.utils.class_weight.compute_class_weight — scikit-learn 0.20.4 documentation (original) (raw)

sklearn.utils.class_weight. compute_class_weight(class_weight, classes, y)[source]¶

Estimate class weights for unbalanced datasets.

Parameters:	class_weight : dict, ‘balanced’ or None If ‘balanced’, class weights will be given byn_samples / (n_classes * np.bincount(y)). If a dictionary is given, keys are classes and values are corresponding class weights. If None is given, the class weights will be uniform. classes : ndarray Array of the classes occurring in the data, as given bynp.unique(y_org) with y_org the original class labels. y : array-like, shape (n_samples,) Array of original class labels per sample;
Returns:	class_weight_vect : ndarray, shape (n_classes,) Array with class_weight_vect[i] the weight for i-th class

Parameters:

class_weight : dict, ‘balanced’ or None If ‘balanced’, class weights will be given byn_samples / (n_classes * np.bincount(y)). If a dictionary is given, keys are classes and values are corresponding class weights. If None is given, the class weights will be uniform. classes : ndarray Array of the classes occurring in the data, as given bynp.unique(y_org) with y_org the original class labels. y : array-like, shape (n_samples,) Array of original class labels per sample;

Returns:

class_weight_vect : ndarray, shape (n_classes,) Array with class_weight_vect[i] the weight for i-th class

References

The “balanced” heuristic is inspired by Logistic Regression in Rare Events Data, King, Zen, 2001.