![]() In the case of Random Forest Regression, it doesn’t predict beyond the range in the training data. But the Random Forest Regression algorithm does not perform a good job as a classification because it does not give precise continuous nature prediction. Random Forest Regression works on a principle that says a number of weakly predicted estimators when combined together form a strong prediction and strong estimation. Random Forest Regression is one of the fastest machine learning algorithms giving accurate predictions for regression problems. Importance & Disadvantage of Random Forest Regression ![]() Understanding the relationship between the predictors and the response. X: teaching method, age, sex, ability, etc. Predicting a systolic blood pressure of a person based on their age, height, weight, etc.
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