compute_class_weight#

sklearn.utils.class_weight.compute_class_weight(class_weight, *, classes, y, sample_weight=None)[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)) or their weighted equivalent if sample_weight is provided. 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.

sample_weightarray-like of shape (n_samples,), default=None

Array of weights that are assigned to individual samples. Only used when class_weight='balanced'.

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])