python中sklearn的樸素貝葉斯方法(sklearn.naive_bayes.GaussianNB)的簡單使用
阿新 • • 發佈:2019-01-03
#測試資料 import numpy as np features_train = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) labels_train = np.array([1, 1, 1, 2, 2, 2]) #引入高斯樸素貝葉斯 from sklearn.naive_bayes import GaussianNB #例項化 clf = GaussianNB() #訓練資料 fit相當於train clf.fit(features_train, labels_train) #輸出單個預測結果 features_test = np.array([-0.8,-1]) labels_test = np.array([1]) pred = clf.predict(features_test) print(pred) #準確度評估 評估正確/總數 #方法1 accuracy = clf.score(features_test, labels_test) #方法2 from sklearn.metrics import accuracy_score accuracy2 = accuracy_score(pred,labels_test) 更多參考:http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html