Random forests or decision tree forests focuses only on ensembles of decision trees. This method combines the base principles of bagging with random feature selection to add additional diversity to the decision tree models. After the ensemble of trees (the forest) is generated, the model uses a vote to combine the trees’ predictions. As the ensembles uses only a small, random portion of the full feature set, random forests can handle extremely large datasets, where the so-called “curse of dimensionality” might cause other models to fail. At the same time, its error rates for most learning tasks are on par with nearly any other method. It is a all purpose model that performs well in most problems, but unlike a decision tree, the model is not easily interpretable. Can handle noisy, missing data as well as categorical or continuous features, but selects only the most important features. Here we will work with the Pima Indians Diabetes database to predict the onset of diabetes base...