Lab 8 Decision Tree, Random Forest, Gradient Boosted Trees, and Cross Validation in, Rapid Miner

Objectives: To learn how to classify data with tree models. To learn how to assess model performance with crossvalidation. Data: Download the house price label excel file from Steps: 1. Load the housepricelabel data into RapidMiner. 2. Build a set of tree models to classify house types based on house ages and prices. Use CrossValidation to assess the performance (including accuracy and kappa) of each model: 2. 1. A decision tree model with a maximal depth of 5 2. 2. A decision tree model with a maximal depth of 10 2. 3. A random forest model with 100 trees and maximal depth of 10 2. 4. A Gradient Boosted trees model with 20 trees and maximal depth of 5 3. Bring more features, such as area, number of bathrooms and bedrooms, and lot size, to the tree models to classify the house type. See how the models performance will change. More about machine learning: http