Applied Machine Learning, Part 3: Hyperparameter Optimization using MATLAB
Machine learning is all about fitting models to data. This process typically involves using an iterative algorithm that minimizes the model error.
- MATLAB for Machine Learning: https://bit.ly/2tUPS0O
- Try it now in your browser: https://bit.ly/2IS82KT
The parameters that control a machine learning algorithm’s behavior are called hyperparameters. Depending on the values you select for your hyperparameters, you might get a completely different model. So, by changing the values of the hyperparameters, you can find different, and hopefully better, models.
This video walks through techniques for hyperparameter optimization, including grid search, random search, and Bayesian optimization. It explains why random search and Bayesian optimization are superior to the standard grid search, and it describes how hyperparameters relate to feature engineering in optimizing a model.
- MATLAB for Machine Learning: https://bit.ly/2tUPS0O
- Try it now in your browser: https://bit.ly/2IS82KT
The parameters that control a machine learning algorithm’s behavior are called hyperparameters. Depending on the values you select for your hyperparameters, you might get a completely different model. So, by changing the values of the hyperparameters, you can find different, and hopefully better, models.
This video walks through techniques for hyperparameter optimization, including grid search, random search, and Bayesian optimization. It explains why random search and Bayesian optimization are superior to the standard grid search, and it describes how hyperparameters relate to feature engineering in optimizing a model.
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