7 Ways to Improve Machine Model Results

Imagine you have prepared for an exam or competition, but in spite of your efforts you do not get the results you want. If you choose to try again, you will likely revise your preparation tactics based on your first experience. In other words, you will fine tune yourself and your techniques to achieve a better result. A Similar approach is used to improve the scores of machine learning models, and in this article, 7 particular methods of improvement will be discussed.

1. Adding more data

Assuming that it is relevant and accurate, there can never be too much data. When advanced machines receive more data, they learn more, self-correct flaws, and produce more desirable results. This is very much similar to us as the more we refer to different sources to prepare for an exam, the better we perform during the test.

2. Treating missing and outlier values

While more data is always more beneficial, the quality of the data must also be taken into consideration. When there are missing values and outliers present in a dataset, the data can often be more detrimental than helpful for machine learning. As a result, it is crucial to identify and correct them in order for machines to learn accurately. Once again using people as an example, imagine preparing for a biology quiz but the only birds you know are penguins…