Ensemble Machine Learning Models in Health and Psychology
Course in Development
Important Update
The offline version of this course is not available anymore. The course will be held online only.
What You'll Learn
- Understand the fundamentals of ensemble learning and why it improves prediction accuracy
- Implement bagging, boosting, and stacking techniques for health and psychology datasets
- Apply Random Forest, Gradient Boosting, and XGBoost algorithms in Python and R
- Evaluate and compare ensemble models using appropriate metrics and cross-validation
- Handle imbalanced datasets and feature selection in ensemble frameworks
- Interpret complex ensemble models for clinical and psychological research applications
Course Sections and Titles
1.
Introduction to Ensemble Learning
2.
Why Ensemble Methods Work
3.
Bias-Variance Tradeoff in Ensemble Models
4.
Bagging: Bootstrap Aggregating
5.
Random Forest: Theory and Applications
6.
Feature Importance in Random Forest
7.
Implementing Random Forest in Python
8.
Implementing Random Forest in R
9.
Boosting Algorithms Overview
10.
AdaBoost: Adaptive Boosting
11.
Gradient Boosting Machines (GBM)
12.
XGBoost: Extreme Gradient Boosting
13.
LightGBM and CatBoost
14.
Hyperparameter Tuning for Ensemble Models
15.
Stacking and Meta-Learning
16.
Voting Classifiers and Regressors
17.
Cross-Validation Strategies for Ensemble Models
18.
Handling Imbalanced Data with Ensemble Methods
19.
Model Evaluation Metrics for Health Data
20.
Interpretability and Explainability (SHAP, LIME)
21.
Case Study: Predicting Mental Health Outcomes
22.
Case Study: Disease Diagnosis with Ensemble Models
23.
Best Practices and Common Pitfalls
24.
Future Directions in Ensemble Learning
This course is currently in development
We're working hard to bring you comprehensive content on ensemble machine learning methods for health and psychology research.
Interested in this course? Contact us to be notified when it launches.