The class weights and sample weights are used in the same way, as a factor
specific to each sample, in the loss function.
In LogisticRegression, it is equivalent to incorporate this factor into a
regularization parameter C specific to each sample.
Tom
2017-08-01 18:30 GMT+02:00 Johnson, Jeremiah
Right, I know how the class_weight calculation is performed. But then
those class weights are utilized during the model fit process in some way
in liblinear, and that¹s what I am interested in. libSVM does
class_weight[I] * C (https://www.csie.ntu.edu.tw/~cjlin/libsvm/); is the
implementation in li
I hope not. And not accoring to the docs...
https://github.com/scikit-learn/scikit-learn/blob/ab93d65/sklearn/linear_model/logistic.py#L947
class_weight : dict or 'balanced', optional
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to h
Hello all,
I'm looking for confirmation on an implementation detail that is somewhere in
liblinear, but I haven't found documentation for yet. When the
class_weights='balanced' parameter is set in LogisticRegression, then the
regularisation parameter for an observation from class I is class_wei
