H2O: Random Forest hyperparameter tuning with H2O in R - R for Data Science

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This video shows how to conduct hyperparameter tuning in the regression setting with Random Forest using the H2O platform in R.

H2O provides a couple of helpful methods for hyperparameter tuning that can be run by executing either a systematic grid search or a random grid search.

In the video you can learn how to work with these methods and how to select the best model with optimal parameters based on their performance after crossvalidation.

This video is part of the series on R for Data Science.

Data Ninjas
R language
RStudio
data science
regression
predictive modeling

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Thanks for the contribution, from what I see is it possible to run R in COLAB, is R integrated or do you need to incorporate it into Colab?

Greetings from Mexico

JorgeRodriguez-mpmt

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