Note: When you create a Regression tree, the argument method = 'class' (for classification) is NOT USED at the end of the rpart or predict list of arguments. # HERE ARE THE SAME ESSENTIAL STEPS for a NUMERIC CONTINUOUS TARGET VARIABLE my Iris

Note: When you create a Regression tree, the argument me thod = 'class ' (for classification) is NOT USED at the end of the xpart or predict list of arguments. \# HERE ARE THE SAME ESSENTIAL STEPS for a NUMERIC CONTINUOUS TARGET VARIABLE mYIris (1:40,51:90,101:140), evaluationbata R Studia, If you then run the above six lines of code, you get the following numeric results: The RMSE (root-mean-square error) is a frequently used measure of the differences between values predicted by a model and the values observed. in this case the RMSE value of 0.3346266 tells us the model's predicted values for the Sepallength value is generally off by 0.33 "units" (whatever measurement units were used for Sepali tength when the data was collected.) Change the two lines that create the training Data and the evaluationData as shown below: trainingData (1:25,51:75,101:125), evaluationData [0(1:25,51:75,101:125)