How to Use a Machine Learning Checklist to Get Accurate Predictions, Reliably

Last Updated on August 15, 2020

How do you get accurate results using machine learning on problem after problem?

The difficulty is that each problem is unique, requiring different data sources, features, algorithms, algorithm configurations and on and on.

The solution is to use a checklist that guarantees a good result every time.

In this post you will discover a checklist that you can use to reliably get good results on your machine learning problems.

Each Data Problem is Different

You have no idea what algorithm will work best on a problem before you start.

Even expert data scientists cannot tell you.

This problem is not limited to the selection of machine learning algorithms. You cannot know what data transforms and what features in the data that if exposed would best present the structure of the problem to the algorithms.

You may have some ideas. You may also have some favorite techniques. But how do you know that the techniques that got you results last
To finish reading, please visit source site