Question

我是scikit learn的新手，我对这个程序试图预测的内容感到困惑。

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)

print(clf.predict([[-0.8, -1]]))

如果我运行这个程序，我会得到：

[1]

据我所知＆＃34; X＆＃34;是培训数据，我不确定是什么＆＃34; Y＆＃34;是。如果我改变：

([[-0.8, -1]])

到

([[-0.8, 1]])

我得到了

[2]

我只需要一点定义。

Answer 1

Y是一个培训标签。函数predict返回预测标签。

Answer 2

clf = GaussianNB() #creating the naive bayes classifier object
clf.fit(X, Y)      #fitting the classifier to the training data points x providing         
                   ## their already known classes in the list y which is of same  
                   #length as X and every element is the class of the   
                   #corresponding element (datapoint) in x                                                             
clf.predict([[-0.8, -1]]) #predicting the class of the testing datapoint[-0.8, -1]

Scikit学习朴素贝叶斯

2 个答案: