7-6 scikit-learn中的PCA、寻找合适的维度

未降维时

scikit-learn 中的PCA¶
import numpy as np
import matplotlib.pyplot as plt

from sklearn import datasets
import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets

digits = datasets.load_digits()
X = digits.data
y = digits.target

from sklearn.model_selection import 

X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=666)

X_train.shape
输出：(1347, 64)

X_test.shape
输出：(450, 64)

%%time
from sklearn.neighbors import KNeighborsClassifier
knn_clf = KNeighborsClassifier()
knn_clf.fit(X_train,y_train)
输出：Wall time: 134 ms

KNeighborsClassifier()
knn_clf.score(X_test,y_test)#得到的识别率很高
输出：0.9866