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_weightdict, ‘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.

classesndarray

Array of the classes occurring in the data, as given by np.unique(y_org) with y_org the original class labels.

yarray-like, shape (n_samples,)

Array of original class labels per sample;

Returns
class_weight_vectndarray, 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.