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.