分类任务中的所有二元预测变量 [英] All binary predictors in a classification task

问题描述

我正在使用R进行分析，我将实现四种算法.

I am performing my analysis using R, I will be implementing four algorithms.

1. RF
2. Log Reg
3. SVM
4. LDA

我有50个预测变量和1个目标变量.我所有的预测变量和目标变量都是二进制数字0s和1s.

I have 50 predictors and 1 target variable. All my predictors and target variable are only binary numbers 0s and 1s.

我有以下问题:

Should I convert them all into factors?
Converting them into factors, and applying RF algorithms give 100% accuracy, I am very much surprised to see that as well.
Also, for other algorithms, how should i treat my variables priorly, before feeding them into my other algorithms.

谢谢

推荐答案

使用adaboost ...

Use adaboost...

研究一下不同的kaggle内核，尤其是梅赛德斯(Mercedes)内核，以实现实现adaboost的想法.

Take a look at different kaggle kernels, especially the Mercedes one, to get the idea of implementing adaboost.

https://www.kaggle.com/c/mercedes -benz-greener-manufacturing/内核

数据集包含数值和因子以及0s，1s.

The dataset is mixed of both numerical and factors and 0s,1s.

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