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, ...

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.

Among these three regression models, random forest regression has the best prediction effect with a model score of 0.8564, which is the best prediction effect among the three models.