Random forests can predict a value that is out of the range of the dependent variable present in the training data is this true?
Answers
Answer:
Random Forest is a popular machine learning model that is commonly used for classification tasks as can be seen in many academic papers, Kaggle competitions, and blog posts. In addition to classification, Random Forests can also be used for regression tasks. A Random Forest’s nonlinear nature can give it a leg up over linear algorithms, making it a great option. However, it is important to know your data and keep in mind that a Random Forest can’t extrapolate. It can only make a prediction that is an average of previously observed labels. In this sense it is very similar to KNN. In other words, in a regression problem, the range of predictions a Random Forest can make is bound by the highest and lowest labels in the training data. This behavior becomes problematic in situations where the training and prediction inputs differ in their range and/or distributions. This is called covariate shift and it is difficult for most models to handle but especially for Random Forest, because it can’t extrapolate.
Answer:
False
Explanation: