sklearn.utils.class_weight.compute_class_weight¶
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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)withy_orgthe 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.