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Outline:Support Vector Machine using R. About Support Vector Machine. Advantages and applications of SVM. Practical implementation of SVM in R. Perform Support Vector Machine on the built-in iris dataset. Load the required libraries and packages. Split the dataset into dependent and independent variables. Create a Support Vector Machine model. Use the pred variable to find the confusion matrix for the model. Plot the data using the SVM model. Disadvantages of SVM.
Support Vector Machine using R. About Support Vector Machine. Advantages and applications of SVM. Practical implementation of SVM in R. Perform Support Vector Machine on the built-in iris dataset. Load the required libraries and packages. Split the dataset into dependent and independent variables. Create a Support Vector Machine model. Use the pred variable to find the confusion matrix for the model. Plot the data using the SVM model. Disadvantages of SVM.
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