Dear all,
I am using Scikit-Learn library and I want to weight all training samples
(according to unbalanced data). According to the tutorial and what I found
in the web, I should use this method:
search = RandomizedSearchCV(estimator, param_distributions,
n_iter=args.iterations, scoring=mae_scorer,n_jobs=1, refit=True,
cv=KFold(X_train.shape[0],
10, shuffle=True, random_state=args.seed), verbose=1,
random_state=args.seed, fit_params={'sample_weight': weights_array})
search.fit(X_trains, y_train)
where "wights_array" is an array containing the weight of each training
sample. After running the code, I was stopped with the following exception:
ValueError: operands could not be broadcast together with shapes (1118,)
(1006,) (1118,)
It is worth noting that the size of "X_trains", "y_train", and
"weights_array" are equal to 1118.
When I changed the number of folds from 10 to 2, the exception was changed
to this one:
ValueError: operands could not be broadcast together with shapes (1118,)
(559,) (1118,)
Do you know what is the problem? I guess the problem is with "KFold"
method. Any idea is appreciated.
Kind Regards,
Hamed
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