Hyperparameter Tuning the Random Forest in Python – Towards Data Science

I have included Python code in this article where it is most instructive. Full code and data to follow along can be found on the project Github page. The best way to think about hyperparameters is like the settings of an algorithm that can be adjusted to optimize performance, just as we might turn the knobs of an AM radio to get a clear signal (or your parents might have!). While model parameters are learned during training -- such as the slope and intercept in a linear regression -- hyperparameters must be set by the data scientist before training. In the case of a random forest, hyperparameters include the number of decision trees in the forest and the number of features considered by each tree when splitting a node.