How to utilise CARET SVM Model in R

Support Vector Machines (SVM) is a type of supervised machine learning algorithm that is used for classification and regression. The caret package in R is a popular package for building machine learning models, and it also includes an SVM model. Here’s how to use the caret package to build an SVM 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 “svmLinear”, “svmRadial” or “svmPoly” for linear, radial or polynomial kernel SVM respectively.

Tune the model: The “train()” function also allows you to tune the model by specifying different parameters such as the regularization parameter C, the kernel to use. 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 accuracy, precision, recall, F1 score and area under the curve (AUC) 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 Support Vector Machines (SVM) models by using the train() and predict() functions. 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.

 

In this Applied Machine Learning Recipe, you will learn: How to utilise CARET SVM Model in R.

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