分类模型的可信度评估代码实践

这一章又完了~

一，代码

from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split

X, y = make_blobs(n_samples=200, random_state=1, centers=2, cluster_std=5)

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=68)
gnb = GaussianNB()
gnb.fit(X_train, y_train)
predict_proba = gnb.predict_proba(X_test)
print('预测准确率形态：{}'.format(predict_proba.shape))
print(predict_proba[:5])

svc = SVC().fit(X_train, y_train)
dec_func = svc.decision_function(X_test)
print(dec_func[:5])

# plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.cool, edgecolors='k')
# plt.show()

二，效果

C:\Users\ccc\AppData\Local\Programs\Python\Python38\python.exe D:/Code/Metis-Org/app/service/time_series_detector/algorithm/ai_test.py
预测准确率形态：(50, 2)
[[0.98849996 0.01150004]
 [0.0495985  0.9504015 ]
 [0.01648034 0.98351966]
 [0.8168274  0.1831726 ]
 [0.00282471 0.99717529]]
[-1.36071347  1.53694862  1.78825594 -0.96133081  1.81826853]

Process finished with exit code 0