Random Forest
A random forest is an ensemble learning method that combines the predictions from multiple decision trees to produce a more accurate and stable prediction. It is a type of supervised learning algorithm that can be used for both classification and regression tasks.
Every decision tree has high variance, but when we combine all of them in parallel then the resultant variance is low as each decision tree gets perfectly trained on that particular sample data, and hence the output doesn’t depend on one decision tree but on multiple decision trees. In the case of a classification problem, the final output is taken by using the majority voting classifier. In the case of a regression problem, the final output is the mean of all the outputs. This part is called Aggregation.
Random Forest Regression Model Working
Random Forest Regression in Python
Random Forest Regression is a versatile machine-learning technique for predicting numerical values. It combines the predictions of multiple decision trees to reduce overfitting and improve accuracy. Python’s machine-learning libraries make it easy to implement and optimize this approach.