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Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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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}') print()
Transcribed Image 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}') print()
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