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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