In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)The model created in the Training step is applied to the current test set (10 %).<br/>The performance is evaluated and sent to the operator results.In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)The model created in the Training step is applied to the current test set (10 %).<br/>The performance is evaluated and sent to the operator results.In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)The model created in the Training step is applied to the current test set (10 %).<br/>The performance is evaluated and sent to the operator results.In the training phase, a model is built on the current training data set. (90 % of data by default, 10 times)The model created in the Training step is applied to the current test set (10 %).<br/>The performance is evaluated and sent to the operator results.Inspect the full example - This process takes a long time to run<br>Modeling Comparison Lab --- You may find the lesson <a href=https://academy.rapidminer.com/learn/video/modeling-challenge-lab">here!</a> ---- This is a hard lab!<br><br>1. Use the CustomerRiskData in the 'Data for Model Comparisons' folder to predict the 'creditworthy' column<br><br>2. Build DT, Neural Net, NB,and SVM models for this data<br><br>3. Compare and determine which is the best suited model for this data<br><br>4. Optional: continue to explore additional models, using Feature Selection, PCA, and Optimization