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Hyperparameter Tuning in R

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Updated 12/2024

Learn how to tune your model's hyperparameters to get the best predictive results.

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RMachine Learning4 hours14 videos47 exercises3,500 XP7,126Statement of Accomplishment

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Course Description

For many machine learning problems, simply running a model out-of-the-box and getting a prediction is not enough; you want the best model with the most accurate prediction. One way to perfect your model is with hyperparameter tuning, which means optimizing the settings for that specific model. In this course, you will work with the caret, mlr and h2o packages to find the optimal combination of hyperparameters in an efficient manner using grid search, random search, adaptive resampling and automatic machine learning (AutoML). Furthermore, you will work with different datasets and tune different supervised learning models, such as random forests, gradient boosting machines, support vector machines, and even neural nets. Get ready to tune!

Prerequisites

Machine Learning with caret in R

1

Introduction to hyperparameters

Parameters vs hyperparameters

Model parameters vs. hyperparameters

Hyperparameters in linear models

What are the coefficients?

Recap of machine learning basics

Machine learning with caret

Resampling schemes

Hyperparameter tuning in caret

Hyperparameters in Stochastic Gradient Boosting

Changing the number of hyperparameters to tune

Tune hyperparameters manually

2

Hyperparameter tuning with caret

Hyperparameter tuning in caret

Finding hyperparameters

Cartesian grid search in caret

Plot hyperparameter model output

Grid vs. Random Search

Grid search with range of hyperparameters

Find train() option for random search

Random search with caret

Adaptive resampling

Advantages of Adaptive Resampling

Adaptive Resampling with caret

3

Hyperparameter tuning with mlr

Machine learning with mlr

Machine Learning with mlr

Modeling with mlr

Grid and random search with mlr

Random search with mlr

Perform hyperparameter tuning with mlr

Evaluating hyperparameters with mlr

Why to evaluate tuning?

Evaluating hyperparameter tuning results

Advanced tuning with mlr

Define advanced tuning controls

Define aggregated measures

Setting hyperparameters

4

Hyperparameter tuning with h2o

Machine learning with h2o

Prepare data for modelling with h2o

Modeling with h2o

Grid and random search with h2o

Grid search with h2o

Random search with h2o

Stopping criteria

Automatic machine learning with H2O

AutoML in h2o

Scoring the leaderboard

Extract h2o models and evaluate performance

Hyperparameter Tuning in R

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