Random Forest Regression: An Ensemble Learning Method For Machine Learning Prediction 1

stable prediction model. The algorithm works by randomly selecting a subset of features from the dataset and then building a decision tree using those features. The decision tree is then used to make predictions on the data. The predictions are then combined to create a more accurate and stable prediction model.

Random Forest Regression is a powerful tool for both regression and classification problems. It is an effective way to reduce overfitting and improve the accuracy of predictions. It is also a great tool for feature selection and can be used to identify important features in a dataset. Random Forest Regression is a powerful and versatile machine learning technique that can be used to solve a variety of problems.