In this tutorial, I explain how to build a Random Forest Classifier for the multi-class classification problem. The IRIS data set is utilized. Preprocessing, selecting the target variable, and splitting the data into a training and test set are done. A Random Forest Classifier is then built using the training set and its performance on both the training set and test set are measured. The results are further explained.
MORE VIDEOS:
Load data in RapidMiner
https://youtu.be/M98QACQug_M
Missing values
https://youtu.be/k2FPcWFCrdI
Decision tree
https://youtu.be/sAH2ltmPZsQ
