End-to-End Machine Learning: manual 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. The user has to run the model for each combination of parameters and evaluate its performance, which can be time-consuming and tedious.
In R, there are several packages that provide functions to perform grid search, such as caret
, mlr
, and tune
, but they do not always provide the flexibility to perform a manual grid search. In such cases, users can use base R functions such as expand.grid
and lapply
to create a manual grid search.
Manual 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 manual 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, manual grid search is a useful technique in R for finding the best set of parameters for a machine learning model. It provides the flexibility and control over the search process, but it requires more time and effort compared to automatic grid search or pre-built functions from packages. 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. Additionally, it is important to plan ahead and set up the grid search in an efficient way, to minimize the number of runs and the time required to complete the search.
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: manual grid search in R.
End-to-End Machine Learning: manual grid search in R
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