In the k-nearest neighbors algorithm, the computation time for classifying samples increases with the value of k. Use %timeit function for jupyter nutebook or Time library for Python console to...

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Extracted text: In the k-nearest neighbors algorithm, the computation time for classifying samples increases with the value of k. Use %timeit function for jupyter nutebook or Time library for Python console to calculate the run time of the KNeighborsClassifier cross-validation for the Digits dataset. Use values of 1, 10 and 20 for k. 1. Compare the results. 2. Display the accuracy and mean of cross-validation score by using following loop. for k in range(1, 20, 2): kfold = KFold(n_splits=10, random_state=11, shuffle=True) knn = KNeighborsClassifier(n_neighbors=k) scores = cross_validate(estimator=knn, X=digits.data, y=digits.target, cv=kfold) print(f'k={k:<2}') print(f'score="" mean="" accuracy="{scores["test_score"].mean():.2%}')" print(f'score="" time="" mean="{scores["score_time"].mean():.5}')">