How to Improve Machine Learning Results
Last Updated on August 16, 2020
Having one or two algorithms that perform reasonably well on a problem is a good start, but sometimes you may be incentivised to get the best result you can given the time and resources you have available.
In this post, you will review methods you can use to squeeze out extra performance and improve the results you are getting from machine learning algorithms.
When tuning algorithms you must have a high confidence in the results given by your test harness. This means that you should be using techniques that reduce the variance of the performance measure you are using to assess algorithm runs. I suggest cross validation with a reasonably high number of folds (the exact number of which depends on your dataset).
Tuning Fork
Photo attributed to eurok, some rights reserved
The three strategies you will learn about in this post are:
- Algorithm Tuning
- Ensembles
- Extreme Feature Engineering
Algorithm Tuning
The place to start is to get better results from algorithms that you already know perform well on your
To finish reading, please visit source site