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Hyperparameter Tuning in R
4.8(59)7k studentsADVANCEDEnglish
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Overview
This advanced R course focuses on perfecting supervised machine learning models through hyperparameter optimization. Using R packages like caret, mlr, and h2o, you learn to implement grid search, random search, adaptive resampling, and AutoML across models including random forests, gradient boosting machines, support vector machines, and neural networks.
Who It Is For
Advanced R practitioners and machine learning engineers looking to maximize model accuracy through automated and efficient hyperparameter tuning.
Key Takeaways
- Utilize caret, mlr, and h2o R packages for hyperparameter optimization.
- Apply grid search, random search, adaptive resampling, and AutoML techniques.
- Tune supervised algorithms including random forests, GBMs, SVMs, and neural networks.
About this course
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!
Price
Free
This course is free to enrol.
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