Hi all! I am playing around with the KNeighboursClassifier class and have a question/concern:
When I use the default parameters the code runs smoothly in 709 ms: %%timeit model = KNeighborsClassifier() model.fit(X_train, y_train) print(model.score(X_valid, y_valid)) Now I don't change anything, except, e.g., the distance class used: %%timeit model = KNeighborsClassifier() model.metrics = 'manhattan' model.fit(X_train, y_train) print(model.score(X_valid, y_valid)) In my understanding the Manhattan distance shouldn't be more expensive than the euclidean distance, but the code above runs at least a few minutes until I decide to interrupt it. (It shouldn't even take 3 or 4 times as long, right?) That also happens, when I just change the p value to 1. Is there something I miss?
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