Predicting Remission Status in Healthcare: A Comparative Analysis of Lasso, Random Forest, and Adaboost Machine Learning Models
In this article, we will explore the following topics:
Using regularized method (Lasso) for predictive variable selection Tuning hyperparameters for tree-based methods Employing the weighted sum of weak learners for boosted classifier Comparing prediction performances and predictors importance Basic Methods Inmagine you possess a dataset comprising 30 biomarker varaibles with 5000+. How would you use it to predict patient’s remission status, i.e. remission or active disease? One common approach that may cross your mind is the logistic regression, as illustrated below:
2024-02-10