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Machine Learning For Dummies by Luca Massaron, John Paul Mueller

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Chapter 23

Ten Ways to Improve Your Machine Learning Models

IN THIS CHAPTER

Understanding the best ways to improve your model

Avoiding snooping and other self-deceptions

Figuring out the best ways to optimize your problem

Getting the right parameters for the best result

Exploring solutions from simplest to most complex

Putting different solutions together

Using one solution to predict another

Creating and engineering new features

Setting less useful features and variables apart

Offering algorithms more chances to learn

Now that your algorithm has finished learning from the data obtained using Python or R, you’re pondering the results from your test set and wondering whether you can improve them or have really reached the best possible outcome. This chapter introduces you to a number of checks and actions that hint at methods you can use to improve machine learning performance and achieve a more general predictor that’s able to work equally well with your test set or new data. This list of ten techniques offers you opportunities to improve the outcome achieved using machine learning algorithms.

remember Monitoring learning performance using a training set helps you keep an eye on how the algorithm is doing. Training results are always too optimistic because, as learning occurs, some data memorization happens as well. The following advice helps you achieve a better result when using a test ...

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