Random forest regression is a type of machine learning algorithm that is used for regression tasks. Regression is a type of supervised learning in which the goal is to predict a continuous outcome variable (such as a real number) based on one or more input features. Random forest regression is an ensemble method, which means that it trains multiple decision trees on random subsets of the data and then averages their predictions to make a final prediction. This can provide a more robust and accurate model than a single decision tree, especially for complex and non-linear data. In random forest regression, the final prediction is made by taking the average of the predicted values from all of the individual decision trees in the ensemble.
Perfecting your machine learning model’s hyperparameters can often feel like hunting for a proverbial needle in a haystack. But with the Random Search algorithm, this intricate process of hyperparameter tuning can be efficiently automated, saving you valuable time and effort. Hyperparameters are properties intrinsic to your model, like the number of estimators in an ensemble … Read more
