scikit-learn 中的不平衡 [英] Imbalance in scikit-learn

问题描述

我在 Python 程序中使用 scikit-learn 来执行一些机器学习操作.问题是我的数据集存在严重的不平衡问题.

是否有人熟悉 scikit-learn 或 Python 中不平衡的解决方案?在 Java 中有 SMOTE 机制.python中有没有并行的东西?

解决方案

这里有一个新方案

https://github.com/scikit-learn-contrib/imbalanced-learn

它包含以下类别的许多算法，包括SMOTE

• 对多数类进行欠采样.
• 对少数类进行过采样.
• 结合过采样和欠采样.
• 创建合奏平衡集.

I'm using scikit-learn in my Python program in order to perform some machine-learning operations. The problem is that my data-set has severe imbalance issues.

Is anyone familiar with a solution for imbalance in scikit-learn or in python in general? In Java there's the SMOTE mechanizm. Is there something parallel in python?

解决方案

There is a new one here

https://github.com/scikit-learn-contrib/imbalanced-learn

It contains many algorithms in the following categories, including SMOTE

• Under-sampling the majority class(es).
• Over-sampling the minority class.
• Combining over- and under-sampling.
• Create ensemble balanced sets.

这篇关于scikit-learn 中的不平衡的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！