End-to-End Machine Learning: random search in R

When training a machine learning model, it’s important to find the best set of parameters that will give the best performance on new, unseen data. One way to do this is by using a technique called “random search.”

Random search is a method of systematically finding the best set of parameters for a model by randomly sampling different combinations of parameters and evaluating their performance. Unlike grid search which considers all possible combinations of parameters, random search only samples a random subset of them. This can be a more efficient way to explore the parameter space and find the optimal parameters.

In R, there are several packages and libraries such as caret, mlr, tune, hyperopt and optim that provide functions to perform random search. These packages offer different ways to perform random search, but the basic idea is the same: to perform random search, you provide the data, the model, and the range of values for each parameter that you want to search through. The package will then randomly sample different combinations of parameters and run the model for them, evaluating its performance using a defined metric.

Random search can be a more efficient and faster way to explore the parameter space and find the optimal parameters compared to grid search and other methods. It is especially useful when the number of parameters to be tuned is large or when the computational resources are limited.

However, it’s important to note that random search, like any other search method, can be computationally expensive, especially when the dataset is large. It’s also important to use the appropriate performance metric to evaluate the model’s performance, and to use cross-validation to ensure that the best parameters are robust and generalize well to new data.

Overall, random search is a useful technique in R for finding the best set of parameters for a machine learning model. It can be faster and more efficient than other methods like grid search, especially when the number of parameters to be tuned is large or when the computational resources are limited. It is important to use the appropriate performance metric to evaluate the model’s performance, and to use cross-validation to ensure that the best parameters are robust and generalize well to new data.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: random search in R.

End-to-End Machine Learning: random search in R

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