Okay let's say I want to use a decision tree and these are because I can do something is choose and then I can choose the tree I can choose j phi j phi is the Heidi tree algorithm, I can choose the classes our class variable here the variable that I want to predict, then I can change the settings also. So batch size is one of our computers our factor is 0.25, minimum number decimal place, number of holes. Pruning maybe I want to set to true and they say things the prune and so on. That I can create okay. Okay, I Christa, then I had a confusion matrix here, precision recall, and and correctly classified instances, isn't it? 4% In Corolla classifies around 5% then I can explore the functions in all this result.

So, view in main window view in separate windows say please upon delivery is about load model save model reapply these model configurations, visualize our classification errors, visualize tree, this is a decision tree so, I can visualize the tree. So, this is a decision tree I can more options here visualize margin, visualize transport cost benefit analysis visualize Costco soap or other result and also our visualized classifier. Visualize the margin visualize the threshold cost benefit analysis and visualize a Costco so this is how I create a decision tree in this vehicle software