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样本还是选用的鸢尾花,iris,多么美丽的花儿
# -*- coding: utf-8 -*- import sklearn from sklearn import naive_bayes import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn import datasets import pandas as pd import numpy def getData_1(): iris = datasets.load_iris() X = iris.data #样本特征矩阵,150*4矩阵,每行一个样本,每个样本维度是4 y = iris.target #样本类别矩阵,150维行向量,每个元素代表一个样本的类别 df1=pd.DataFrame(X, columns =['SepalLengthCm','SepalWidthCm','PetalLengthCm','PetalWidthCm']) df1['target']=y return df1 df=getData_1() X_train, X_test, y_train, y_test = train_test_split(df.iloc[:,0:3],df['target'], test_size=0.3, random_state=42) print X_train, X_test, y_train, y_test model = naive_bayes.GaussianNB() # 高斯贝叶斯 model.fit(X_train,y_train) predict=model.predict(X_test) print predict print y_test.values a=0 for i in range(len(predict)): if predict[i] == y_test.values[i]: a=a+1 score=float(a)/len(predict) print '贝叶斯准确率:%3f' %(score) print '贝叶斯:{:.3f}'.format(model.score(X_test, y_test))
结果:
predict:[1 0 2 1 2 0 1 2 1 1 2 0 0 0 0 2 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 1 1
0 0 0 1 1 2 0 0]
y_test.values:[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1
0 0 0 2 1 1 0 0]
贝叶斯准确率:0.888889
贝叶斯:0.889