End-to-End Machine Learning: optimal parameter 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 “parameter search.”

Parameter search is a method of systematically finding the best set of parameters for a model, typically by training a model with different sets of parameters and evaluating its performance. The goal of parameter search is to find the optimal set of parameters that will give the best performance on new, unseen data.

In R, there are several packages and libraries such as caret, mlr, tune, hyperopt and optim that provide functions to perform parameter search. These packages offer different ways to perform parameter search, but the basic idea is the same: to perform parameter 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 run the model for different combinations of parameters and evaluate its performance using a defined metric.

Optimal parameter search is a powerful tool that can help to improve the performance of a machine learning model by finding the best set of parameters. It can be used with different types of models such as linear and non-linear models, and it can save a lot of time and effort compared to manual grid search.

However, it’s important to note that optimal parameter search can be computationally expensive, especially when the dataset is large or the number of parameters to be searched through 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, optimal parameter search is a useful technique in R for finding the best set of parameters for a machine learning model. It can save time and effort compared to manual grid search and can help to improve the performance of the model. 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: optimal parameter search in R.

End-to-End Machine Learning: optimal parameter search in R

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.