Question

我正在尝试拟合机器学习NB模型，并且一切都很干净，但是可以将我的功能参考标签以适应模型：


labels = [[0,0,0,1,1,0],[0,0,1,0,1,1],...]]

features = [[[0.1,0.2,0.3,0.4,0.5],[0.11,0.21,0.31,0.41,0.51],[0.12,0.22,0.32,0.42,0.52],[0.12,0.22,0.32,0.42,0.52],[0.12,0.22,0.32,0.43,0.53]],[0.13,0.23,0.33,0.43,0.53]...]]]

在我的问题中，[0.1,0.2,0.3,0.4,0.5]必须引用标签中的第一个0，因此分类器给出了否，[0.11,0.21,0.31,0.41,0.51]引用了第二个0 ，也为否，[0.12,0.22,0.32,0.42,0.52]表示前1个，因此分类器为是。

如何拟合NB分类器或重新排列列表以适合模型？

非常感谢您。

Answer 1

您可能可以利用精彩的numpy库，通过该库可以以多种不同方式重新组织输入数据的形状。可能的决定之一如下：

import numpy as np

from sklearn.naive_bayes import GaussianNB

labels = [[0,0,0,1,1,0],
          [0,0,1,0,1,1]]

features = [[[0.1,0.2,0.3,0.4,0.5],
             [0.11,0.21,0.31,0.41,0.51],
             [0.12,0.22,0.32,0.42,0.52],
             [0.12,0.22,0.32,0.42,0.52],
             [0.12,0.22,0.32,0.43,0.53],
             [0.13,0.23,0.33,0.43,0.53]],
           [[0.1,0.2,0.3,0.4,0.5],
             [0.11,0.21,0.31,0.41,0.51],
             [0.12,0.22,0.32,0.42,0.52],
             [0.12,0.22,0.32,0.42,0.52],
             [0.12,0.22,0.32,0.43,0.53],
             [0.13,0.23,0.33,0.43,0.53]]]

labels = np.ravel(labels)
features = np.reshape(features, (-1, 5))

gnb = GaussianNB()
gnb.fit(features, labels)

如何将我的功能引用到标签上？朴素贝叶斯分类器

1 个答案: