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Predicting Used Car Prices with Boosted Trees: Return to the ToyotaCorolla 1000.imp data you saved in Question 2 and refit the partition model, (Hint Use the Redo = Relaunch Analysis button in the partition dialog). This time, choose "Boosted Trees" from the dialog window, Use the default settings. (Note: To generate the same results, first set the random seed to 123 and select the box "Suppress Multithreading") . How does the boosted tree behave in terms of the prediction error relative to the second model in Question 1 and the bootstrap forest. Save the prediction formula for this model to the data table. RSquare and RMSE on Validation sets are better for the boosted tree than for the bootstrap forest. (TRUE/FALSE?) [ Select ] Compared to the boosted tree, RMSE and RSquare for the validation set are better for the second model in Question 1. (TRUE/FALSE?) [ Select ] Save the script to data file (Red triangle option: Save Script> To Data Table) Save Prediction formula (Red triangle option Save Columns>Save Prediction Formula) Save the data file ToyotaCorolla1000.jmp. You will submit the saved file in Question 5 (File>Save)ToyotaCorolla 1000 - JMP Pro File Edit Tables Rows Cols DOE Analyze Graph Tools View Window Help ToyotaCorolla 1000 Source Doors QuartTax Weight Validation Decision Tree of Price 210 1165 Training Bootstrap Forest of Price 2 210 1 165 Validation W 210 1165 Validation Columns (16/1) A 210 1165 Training 210 1170 Training Price 210 1170 Training Age 210 1245 Validation Mileage 210 1245 Validation Fuel Type 100 1185 Training Horse Power 185 1105 Training L Metalic Automatic 100 1185 Validation CC 100 1185 Training Doors 100 1185 Validation QuartTax 100 1 185 Validation Weight 100 Validation K 1 185 Validation Price Predictor # k 100 1185 Validation Price Predictor 2 4- >* 100 1 185 Training Price Predictor 3 #- >K 85 1105 Training Price Predictor 4 # * 19 1065 Training 85 1105 Training ROWS 85 1 105 Training All rows 1,000 100 1170 Validation Selected 85 1120 Training Excluded 85 1120 Validation Hidden Labeled 85 1120 Validation 26 85 1120 Training