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本文主要介绍如何使用python的sk-learn机器学习框架搭建一个或多个:文本分类的机器学习模型,如果有毕业设计或者课程设计需求的同学可以参考本文。本项目使用了决策树和随机森林2种机器学习方法进行实验,完整代码在最下方,想要先看源码的同学可以移步本文最下方进行下载。 博主也参考过文本分类相关模型的文章,但大多是理论大于方法。很多同学肯定对原理不需要过多了解,只需要搭建出一个可视化系统即可。
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【代码分享】基于python的文本分类(sklearn-决策树和随机森林实现).rar (9个子文件)
data
原始数据.xlsx 22KB
train_data.xlsx 18KB
trainlabel_list.npy 208B
需求.png 26KB
model
model_dtc.m 9KB
model_word2vec.m 1.78MB
model_rf_grid.m 2.51MB
模型预测.ipynb 4KB
decision_tree.ipynb 16KB
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