Скачать или смотреть Advance Machine Learning Assignment 1 : Comparing Ensemble vs Single Models

This assignment focuses on comparing ensemble learning models with single machine learning models to understand their differences in performance, accuracy, and generalization.

Single models such as Decision Trees, Logistic Regression, or Support Vector Machines rely on a single algorithm to make predictions. They are simple and easy to interpret but may suffer from issues like overfitting or underfitting.

Ensemble models, on the other hand, combine multiple base learners to form a stronger model. Techniques like Bagging (used in Random Forests) and Boosting (used in AdaBoost, Gradient Boosting, and XGBoost) help reduce errors, improve accuracy, and enhance robustness.

In this assignment, students will implement both single and ensemble models on a chosen dataset and evaluate them using suitable metrics such as accuracy, precision, recall, F1-score, or mean squared error. The comparison will highlight how ensemble methods generally provide better performance and stability, while single models remain faster and easier to interpret.

The key objective is to develop a deeper understanding of when and why ensemble learning methods outperform individual models in solving real-world machine learning problems.