How to Create, Score and Test Random Forest Models in R

In this video you will learn how to quickly and easily build highly accurate random forest models in R. this video is a complete walk-through tutorial that starts with loading in the correct libraries and packages (like the caret R package for machine learning and predictive analysis) and then it walks you through creating a training set, building the random forest in r, scoring and then going back and putting our predictive data back into the original data set to compare it to the original column to test our predictive model.

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For this example we use the iris data set which is publicly available. Then what we do is we take one of the classes of species and we predict based on this. What is really interesting is that we end up with a 95 to 96% accuracy based on our model (rfr). I also show you how to tweak the model to try and exert higher accuracy out of it by increasing the number of trees and more.

With the ideas introduced in this video you will be able to take a data set and quickly see if there are columns with correlations that would be conducive to a random forest algorithm being run on them. If you're interested in learning data science and machine learning, then you'll definitely want to watch this video on building, scoring and testing highly accurate random forest models in R.

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Видео How to Create, Score and Test Random Forest Models in R канала Tech Know How