How to utilise CARET Regularised Regression Model in R

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How to utilise CARET Regularised Regression Model in R

Regularized regression is a type of regression that helps to prevent overfitting by adding a penalty term to the loss function. The caret package in R is a popular package for building machine learning models, and it also includes regularized regression models such as Ridge and Lasso. Here’s how to use the caret package to build a regularized regression model in R:

Prepare the data: The first step is to prepare the data by loading it into R and splitting it into training and testing sets. The training set is used to train the model and the testing set is used to evaluate its performance.

Build the model: The next step is to build the model using the “train()” function from the caret package. The train() function takes in the following inputs: the training data, the formula of the model, and the method of model fitting which in this case is “glmnet” for Ridge and Lasso regularization.

Tune the model: The “train()” function also allows you to tune the model by specifying different parameters such as the regularization parameter lambda. By tuning the model, you can improve its performance on the test set.

Evaluate the model: Once the model is built and tuned, you can use the “predict()” function to make predictions on the test set and evaluate the model’s performance. You can use performance metrics such as mean squared error (MSE) or R-squared to evaluate the model’s performance.

Use the model: Once you are satisfied with the model’s performance, you can use it to make predictions on new data.

In summary, the caret package in R makes it easy to build, tune, and evaluate regularized regression models such as Ridge and Lasso by using the train() and predict() functions. By regularizing the model, it helps to prevent overfitting, and by tuning the model, you can improve its performance on the test set. Once the model is built, you can use it to make predictions on new data.

 

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How to utilise CARET Regularised Regression Model in R

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