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
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
classicML-0.2.2.tar.gz
(12.7 kB
view hashes)
Built Distribution
classicML-0.2.2-py3-none-any.whl
(22.1 kB
view hashes)
Close
Hashes for classicML-0.2.2-py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 2c2c6a0e86673f8390d3d4223fb701da8c649e3a4f10a2fcf1d14c96eb6c2bca |
|
| MD5 | 4463c9f92a8381d89b8b0d85038c5d75 |
|
| BLAKE2b-256 | fc25630d10966111c3d8c916b677ba4c0745ec2f6dae1faa590b40a731706d64 |
