在4-3中使用了分类准确度accuracy来评判KNN算法的性能。
使用accuracy来评价KNN算法对手写数据集的分类效果
加载手写数据集
from sklearn import datasets
digits = datasets.load_digits()
digits的内容如下：
输入：digits.keys()
输出：dict_keys(['data', 'target', 'target_names', 'images', 'DESCR'])
输入：print(digits.DESCR)
输出：digits的官方说明
输入：digits.data.shape()
输出：(1797, 64)
输入：digits.target.shape()
输出：(1797,)
输入：digits.target_names
输出：array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
输入：
some_digit = X[666]
some_digit_image = some_digit.reshape(8, 8)
plt.imshow(some_digit_image, cmap = matplotlib.cm.binary)
plt.show()
train_test_split + KNN + accuracy
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_ratio=0.2)
my_knn_clf = KNNClassifier(k=3)
my_knn_clf.fit(X_train, y_train)
y_predict = my_knn_clf.predict(X_test)
accuracy = sum(y_predict == y_test) / len(y_test)
scikit-learn中的accuracy_score
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=666)
from sklearn.neighbors import KNeighborsClassifier
knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train, y_train)
y_predict = knn_clf.predict(X_test)
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_predict)
