Using %timeit To Calculate the Run Time of the KNeighborsClassifier Cross-Validation for the Digits

Requirements

In the k-nearest neighbors algorithm, the computation time for classifying samples increases with the value of k. Use %timeit to calculate the run time of the KNeighborsClassifier cross-validation for the Digits dataset. Use values of 1, 10 and 20 for k. Compare the results.


Import Libraries

from sklearn import (datasets, metrics,
                     model_selection as skms,
                     naive_bayes, neighbors)

Load Dataset

%timeit -r1 datasets.load_iris()

Output:

The slowest run took 8.04 times longer than the fastest. This could mean that an intermediate result is being cached. 1000 loops, best of 1: 1.07 ms per loop


iris = datasets.load_iris()

Split Dataset

from sklearn.model_selection import train_test_split
(iris_train_ftrs, iris_test_ftrs,iris_train_tgt,iris_test_tgt) = train_test_split(iris.data,iris.target,test_size=.25)

Fit Into Model

%%timeit -r1
knn = neighbors.KNeighborsClassifier(n_neighbors=3)
fit = knn.fit(iris_train_ftrs, iris_train_tgt)
preds = fit.predict(iris_test_ftrs)
metrics.accuracy_score(iris_test_tgt, preds)

Output:

The slowest run took 6.08 times longer than the fastest. This could mean that an intermediate result is being cached. 100 loops, best of 1: 2.95 ms per loop


knn = neighbors.KNeighborsClassifier(n_neighbors=3)
%timeit -r1 fit = knn.fit(iris_train_ftrs, iris_train_tgt)

Output:

The slowest run took 15.41 times longer than the fastest. This could mean that an intermediate result is being cached. 1000 loops, best of 1: 336 µs per loop



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