Weighted Distance In Sklearn Knn

I'm making a genetic algorithm to find weights in order to apply them to the euclidean distance in the sklearn KNN, trying to improve the classification rate and removing some char

Solution 1:

There is indeed another way, and it's inbuilt into scikit-learn (so should be quicker). You can use the wminkowski metric with weights. Below is an example with random weights for the features in your training set.

knn = KNeighborsClassifier(metric='wminkowski', p=2, 
                           metric_params={'w': np.random.random(X_train.shape[1])})