• Graph the mean squared error against the number of trees for a random forest regression model for maximum heart rate achieved using age (age), sex (sex), chest pain type (cp), resting blood pressure (trestbps), cholesterol measurement (chol), resting electrocardiographic measurement (restecg), exercise-induced angina (exang), slope of peak exercise (slope), and number of major vessels (ca). Use a maximum of 80 trees. Use set.seed(511038).

set.seed(511038) library(randomForest) model_heartdataclass1 <- randomForest(target ~ age+sex+cp+trestbps+chol+restecg+exang+slope+ca, data=train.data, ntree = 80)

RMSE = function(pred, obs) { return(sqrt( sum( (pred - obs)^2 )/length(pred) ) ) }

print("======================================================================================================================") print('Root Mean Squared Error: TRAINING set based on random forest model built using 80 trees') pred <- predict(model_heartdataclass1, newdata=train.data, type='response') RMSE(pred, train.data$target)

print("======================================================================================================================") print('Root Mean Squared Error: TESTING set based on random forest model built using 80 trees') pred <- predict(model_heartdataclass1, newdata=test.data, type='response') RMSE(pred, test.data$target)

[1] "======================================================================================================================" [1] "Root Mean Squared Error: TRAINING set based on random forest model built using 80 trees"

Warning message in Ops.factor(pred, obs): "'-' not meaningful for factors"

[1] "======================================================================================================================" [1] "Root Mean Squared Error: TESTING set based on random forest model built using 80 trees"

Warning message in Ops.factor(pred, obs): "'-' not meaningful for factors"