The demo creates a random forest regression model, evaluates the model accuracy on the training and test data, and then uses the model to predict the target y value for x = [-0.1660, 0.4406, -0.9998, ...

David Muchlinski, David Siroky, Jingrui He, Matthew Kocher, Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data, Political Analysis, Vol. 24, No. 1 ...

Therefore, random forest regression is a very effective method for health insurance prediction. The next model is the linear regression model with a model score of 0.7584.

Random forests are quite robust with respect to m, and rules of thumb such as using m = p /3 for regression and m = √ p for classification are sometimes used 7.