Mental Health Classification and Diagnosis System Based on Random Forest Algorithm

Boosting algorithm is a typical representative of ensemble learning models, which combines multiple weak learners into a strong learner to improve learning performance. The basic principle is that each weak learner will learn from the errors of the previous weak learner, thereby improving learning performance. The random forest algorithm is an ensemble algorithm based on decision trees as sub models, which can be used in scenarios such as classification and regression. It is an extended variant of the Bagging algorithm. This article is based on ensemble learning models and first studies the Bagging algorithm, including its principles and processes; Then, based on the Bagging algorithm, construct a random forest classification algorithm model; Finally, through simulation experiments, the random forest algorithm model was validated based on six types of mental health, including ‘‘Introduction, Independent, Depressed, Anxiety, Resolve, Paranoia The results show that the random forest algorithm not only effectively avoids overfitting problems, but also improves classification accuracy, and has broad application prospects in the field of mental health classification and diagnosis.