Random Forest Model [Biostatistics & Machine Learning] 

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Random Forest is a powerful machine learning algorithm widely used in biostatistics for predictive modeling and feature selection. It operates by constructing a multitude of decision trees during training and outputs the mode of the classes (classification) or mean prediction (regression) of the individual trees.
Each tree is trained on a random subset of the data and features, reducing overfitting and increasing robustness. In biostatistics, Random Forest can be applied to various tasks such as disease prediction, drug response modeling, and identifying biomarkers. Its ability to handle high-dimensional data and complex interactions makes it particularly suitable for analyzing biological and medical datasets. Additionally, it provides insights into variable importance, aiding researchers in identifying key factors influencing the outcome of interest.
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