End-to-End Machine Learning: custom grid search in R

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End-to-End Machine Learning: custom grid 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 “grid search.”

Grid search is a method of systematically working through different combinations of parameters, typically by training a model with a given set of parameters and evaluating its performance. In manual grid search, the user has to specify the range of values for each parameter, the evaluation metric and the way the model is trained and evaluated.

In R, there are several packages that provide functions to perform grid search, such as caret, mlr, and tune. However, these packages do not always provide the flexibility to perform a custom grid search. In such cases, users can use base R functions, such as expand.grid and lapply to create a custom grid search.

Custom grid search can be useful when the user wants to use a different evaluation metric, or when the user wants to add a custom preprocessing step or a custom post-processing step. Additionally, it allows the user to have more control over the search process and to fine-tune the search according to the specific needs of the problem.

However, it’s important to note that custom grid search can be more time-consuming than using pre-built functions from packages, especially when the number of parameters to be tuned 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, custom grid search is a useful technique in R for finding the best set of parameters for a machine learning model. It can provide more flexibility and control over the search process and is especially useful when the user wants to use a different evaluation metric or when the user wants to add a custom preprocessing step or a custom post-processing step. 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: custom grid search in R.



End-to-End Machine Learning: custom grid search in R

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