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 of shape (n_samples,): Array of original class labels per sample.

Returns:

class_weight_vectndarray of 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.

Examples

>>> import numpy as np
>>> from sklearn.utils.class_weight import compute_class_weight
>>> y = [1, 1, 1, 1, 0, 0]
>>> compute_class_weight(class_weight="balanced", classes=np.unique(y), y=y)
array([1.5 , 0.75])