The exercise description says to use 1, 10 and 20 for the values of k. These should be 1, 11 and 21 since you should always use odd values to avoid ties. In [ ]: from sklearn.datasets import load_digits In [ ]: digits = load_digits() In [ ]: knn = KNeighborsClassifier(n_neighbors=1) In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold) In [ ]: knn = KNeighborsClassifier(n_neighbors=11) In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold) In [ ]: knn = KNeighborsClassifier(n_neighbors=21) In [ ]: Xtimeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold) In [ ]: from sklearn.model_selection import cross_validate In [ ]: 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() Activ Go to
The exercise description says to use 1, 10 and 20 for the values of k. These should be 1, 11 and 21 since you should always use odd values to avoid ties. In [ ]: from sklearn.datasets import load_digits In [ ]: digits = load_digits() In [ ]: knn = KNeighborsClassifier(n_neighbors=1) In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold) In [ ]: knn = KNeighborsClassifier(n_neighbors=11) In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold) In [ ]: knn = KNeighborsClassifier(n_neighbors=21) In [ ]: Xtimeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold) In [ ]: from sklearn.model_selection import cross_validate In [ ]: 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() Activ Go to
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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![The exercise description says to use 1, 10 and 20 for the values of k. These should be 1, 11 and 21 since you should always use odd values to avoid ties.
In [ ]: from sklearn.datasets import load_digits
In [ ]: digits = load_digits()
In [ ]: knn = KNeighborsClassifier(n_neighbors=1)
In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold)
In [ ]: knn = KNeighborsClassifier(n_neighbors=11)
In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold)
In [ ]: knn = KNeighborsClassifier(n_neighbors=21)
In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold)
In [ ]: from sklearn.model_selection import cross_validate
In [ ]: 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()
Activ
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Transcribed Image Text:The exercise description says to use 1, 10 and 20 for the values of k. These should be 1, 11 and 21 since you should always use odd values to avoid ties.
In [ ]: from sklearn.datasets import load_digits
In [ ]: digits = load_digits()
In [ ]: knn = KNeighborsClassifier(n_neighbors=1)
In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold)
In [ ]: knn = KNeighborsClassifier(n_neighbors=11)
In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold)
In [ ]: knn = KNeighborsClassifier(n_neighbors=21)
In [ ]: %timeit scores = cross_val_score(estimator=knn, X=digits.data, y=digits.target, cv=kfold)
In [ ]: from sklearn.model_selection import cross_validate
In [ ]: 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()
Activ
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