For the following data set using Binary target - 10 year risk of Heart Disease Create a training set/validation set/test set (70%/10%/20%)
• Build two classifiers for your data: o Use rpart to build a classification tree as one of the classifiers o Build one other classifier of your choice:
▪ such as a Naïve Bayes classifier
▪ a logistic regression model o Use a training set – build all classifiers using same training set.
• Optional: As part of the model building, you may optionally wish to tune the parameters of classifiers (e.g. compare different sets of inputs in logistic regression/naïve Bayes classifiers) o Please use the validation set for this o Please note that rpart already performs 𝑘-fold cross-validation as part of the package - so this validation set will not be required to build the classification tree.
• Evaluate the models using Re-substitution error rate / hold outs /Repeated K-fold cross Validation / etc o Use the test set – evaluate all models using the same test set.
• Make a recommendation as to which classifier performs the best for your dataset and what you’ve based this decision on.
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