An easy-to-use ML framework
Project description
classicML
Python自制经典机器学习框架
version v0.1
添加决策树 决策树支持离散值、连续值;基于信息熵、信息增益、基尼指数划分;支持预剪枝和后剪枝;暂不支持多变量决策树和缺失值处理(建议在读入数据集之前处理)
version v0.2
添加神经网络 神经网络暂仅支持交叉熵损失函数;支持的优化器有GradientDescent、SGD、Adam
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