Consider the Following Code and Output: in [57]: for K

Consider the following code and output: In [57]: for k in range(1, 20, 2) :
) ..: kfold = KFold(n_splits=10, random_state=11, shuffle=True)
) ..: knn = KNeighborsClassifier(n_neighbors=k)
) ..: scores = cross_val_score(estimator=knn,
) ..: X=digits.data, y=digits.target, cv=kfold)
) ..: print(f'k={k:<2}; mean accuracy={scores.mean() :.2%}; ' +
) ..: f'standard deviation={scores.std() :.2%}')
) ..:
K=1 ; mean accuracy=98.83%; standard deviation=0.58%
K=3 ; mean accuracy=98.78%; standard deviation=0.78%
K=5 ; mean accuracy=98.72%; standard deviation=0.75%
K=7 ; mean accuracy=98.44%; standard deviation=0.96%
K=9 ; mean accuracy=98.39%; standard deviation=0.80%
K=11; mean accuracy=98.39%; standard deviation=0.80%
K=13; mean accuracy=97.89%; standard deviation=0.89%
K=15; mean accuracy=97.89%; standard deviation=1.02%
K=17; mean accuracy=97.50%; standard deviation=1.00%
K=19; mean accuracy=97.66%; standard deviation=0.96%
Which of the following statements is false?

A) The loop creates KNeighborsClassifiers with odd k values from 1 through 19 and performs k-fold cross-validation on each.
B) The k value 7 in kNN produces the most accurate predictions for the Digits dataset.
C) The accuracy tends to decrease for higher k values.
D) Compute time grows with k, because k-NN needs to perform many more calculations to find the nearest neighbors.