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ASIN : B0BRGVQY9B
Publisher : Springer (January 1, 2023)
Publication date : January 1, 2023
Language : English
File size : 56984 KB
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
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Print length : 507 pages
Hyperparameter Tuning for Machine and Deep Learning with R: A Practical Guide
Hyperparameter tuning is a crucial step in the process of building machine and deep learning models. It involves finding the best values for the hyperparameters of a model in order to improve its performance. In this practical guide, we will explore the concept of hyperparameter tuning and how it can be done using R.
What are Hyperparameters?
Hyperparameters are parameters that are set before the training process begins. They control aspects of the learning algorithm and have a significant impact on the performance of the model. Examples of hyperparameters include the learning rate, number of hidden layers, and batch size.
Why is Hyperparameter Tuning Important?
Hyperparameter tuning is important because it can significantly improve the performance of a model. By finding the best values for the hyperparameters, we can ensure that the model is able to learn effectively and make accurate predictions.
How to Perform Hyperparameter Tuning with R
There are several ways to perform hyperparameter tuning with R. One popular method is grid search, where we define a grid of hyperparameter values and train the model with each combination of values. We then select the combination that gives the best performance.
Another common method is random search, where we randomly sample values from a predefined range of hyperparameters and train the model with each sampled value. This can be a more efficient method than grid search, as it allows us to explore a wider range of values.
In addition to grid search and random search, there are also more advanced techniques such as Bayesian optimization and genetic algorithms that can be used for hyperparameter tuning.
In this guide, we will walk through a step-by-step example of how to perform hyperparameter tuning with R using the caret package. We will demonstrate how to define a grid of hyperparameters, train a model with each combination of values, and evaluate the performance of the model.
By the end of this guide, you will have a better understanding of hyperparameter tuning and how it can be applied to improve the performance of your machine and deep learning models in R. Stay tuned for the next post where we will dive deeper into the world of hyperparameter tuning and explore more advanced techniques.
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