sklearn.utils.class_weight.compute_class_weight

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 by n_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 by np.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.