How to utilise CARET Linear Regression model in R

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How to utilise CARET Linear Regression model in R

Linear regression is a statistical method that is used to predict a continuous target variable based on one or more predictor variables. The caret package in R is a popular package for building machine learning models, and it also includes a linear regression model. Here’s how to use the caret package to build a linear 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 “lm” (Linear Model)

Tune the model: The “train()” function also allows you to tune the model by specifying different parameters such as the regularization parameter. The regularization parameter helps to prevent overfitting by adding a penalty term to the loss function. 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 linear regression models. By using the train() and predict() functions, you can quickly build a model, tune its parameters, and evaluate its performance. Once the model is built, you can use it to make predictions on new data.


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How to utilise CARET Linear Regression model in R

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