k 近傍法：怠惰学習アルゴリズム

Coding

code: Python

import numpy as np

from sklearn import datasets

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler

from sklearn.svm import SVC

iris = datasets.load_iris()

X = iris.data[:, 2, 3]

y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)

sc = StandardScaler()

sc.fit(X_train)

X_train_std = sc.transform(X_train)

X_test_std = sc.transform(X_test)

from sklearn.neighbors import KNeighborsClassifier

# k近傍法のインスタンスを生成

knn = KNeighborsClassifier(n_neighbors=5, p=2, metric='minkowski')

knn.fit(X_train_std, y_train)

X_combined_std = np.vstack((X_train_std, X_test_std))

y_combined = np.hstack((y_train, y_test))

plot_decision_regions(X=X_combined_std, y=y_combined, classifier=knn, test_idx=range(105, 150))

plt.xlabel('petal length standardized')

plt.ylabel('petal with standardized')

plt.legend(loc='upper left')

plt.tight_layout()

plt.show()

https://gyazo.com/2037b4bc9532bbe4bf2c88e4a0a6c046