Tune Machine Learning Algorithms in R (random forest case study)

Last Updated on July 31, 2020

It is difficult to find a good machine learning algorithm for your problem. But once you do, how do you get the best performance out of it.

In this post you will discover three ways that you can tune the parameters of a machine learning algorithm in R.

Walk through a real example step-by-step with working code in R. Use the code as a template to tune machine learning algorithms on your current or next machine learning project.

Kick-start your project with my new book Machine Learning Mastery With R, including step-by-step tutorials and the R source code files for all examples.

Let’s get started.

Get Better Accuracy From Top Algorithms

It is difficult to find a good or even a well-performing machine learning algorithm for your dataset.

Through a process of trial and error you can settle on a short list of algorithms that show promise, but how do you know which
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