How to optimise one of the most used Machine Learning models. In this quick article, we will explore some of the nitty-gritty optimisations of Random Forests, along with what each hyper-parameter is, and which ones are worth optimising.
Random Forest are an awesome kind of Machine Learning models. They solve many of the problems of individual Decision trees, and are always a candidate to be the most accurate one of the models tried when building a certain application.
If you don’t know what Decision Trees or Random Forest are do not have an ounce of worry; I got you covered with the following articles. Take a quick look and come back here.
In this quick article, we will explore some of the nitty-gritty optimisations of Random Forests, along with what each hyper-parameter is, and which ones are worth optimising.
The most important hyper-parameters of a Random Forest that can be tuned are:
Alright, now that we know where we should look to optimise and tune our Random Forest, lets see what touching some of these parameters does.
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In this tutorial on "Data Science vs Machine Learning vs Artificial Intelligence," we are going to cover the whole relationship between them and how they are different from each other.
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