Variable selection

Written by Evispot. Posted in Glossary, v.

Adding variables to your data set can improve the accuracy of your AI model, especially when the model is too simple to fit the existing data properly. However, it is important to focus on variables that are relevant to the problem you’re trying to solve and to avoid focusing on those that contribute nothing. Good variable selection eliminates irrelevant or redundant columns from your data set without sacrificing accuracy.

The benefits of variable selection for AI include:

  • Reducing the chance of overfitting
  • Improving algorithm run speed by reducing the CPU, I/O, and RAM load the production system requires to build and use the model by lowering the number of operations needed to read and preprocess data and perform data science.
  • Increasing the model´s interpretability by revealing the most informative factors driving the model´s outcome

In the evispot AI platform recursive variable elimination is used to determine which variables are most important. The AI algorithm and hyperparameter settings that are used depends on the uploaded data.

© Evispot 2022 All rights reserved.

Machine learning in credit decisions

Leveraging machine learning for smarter lending and obtain insights into the technology behind 100% transparent machine learning models.

A link to download the file will be sent to your inbox.

Never see this message again.