Sean Jho

朴素贝叶斯

 ·  2 分钟阅读  ·  AI 与机器学习

目录

2017 年 8 月 10 日 19:17 朴素贝叶斯属于贝叶斯算法中的一个分支。sklearn.naive_bayes.GaussianNB 用于分类场景。属于监督学习。 应用分为三个步骤： 实例化算法模块 样本训练 fit 预测 predict （计算准确度，需要提供test数据的实际标签）score 样例：

import numpy as np

X

np . array([[

1 ,

1 ], [

2 ,

1 ], [

3 ,

2 ], [ 1 , 1 ], [ 2 , 1 ], [ 3 , 2 ]])

Y

np . array([ 1 , 1 , 1 , 2 , 2 , 2 ])

from sklearn.naive_bayes import GaussianNB

clf

GaussianNB()

clf . fit(X, Y) GaussianNB()

print clf . predict([[

0.8 ,

1 ]]) [1] 样例二，包含测试准确度： def NBAccuracy(features_train, labels_train, features_test, labels_test): """ compute the accuracy of your Naive Bayes classifier """

import the sklearn module for GaussianNB

from sklearn.naive_bayes import GaussianNB

create classifier

clf = GaussianNB()

fit the classifier on the training features and labels

clf.fit(features_train, labels_train)

use the trained classifier to predict labels for the test features

pred = clf.predict(features_train)

### calculate and return the accuracy on the test data
### this is slightly different than the example,
### where we just print the accuracy
### you might need to import an sklearn module

accuracy = clf.score(features_test, labels_test) return accuracy 计算预测准确度还有一个通用方法：用预测值与实际标签对比：

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