📅 2025-06-24 — Session: Reviewed and Outlined Machine Learning Concepts
🕒 16:40–17:00
🏷️ Labels: Classification, Regression, Decision_Trees, Linear_Regression, Machine_Learning, Scikit-Learn
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal
The primary objective of this session was to review and outline key machine learning concepts, focusing on classification metrics, regression errors, decision trees, and linear regression models.
Key Activities
- Classification Metrics: Discussed the importance of selecting appropriate metrics for evaluating classification models, such as confusion matrices, precision, recall, F1 score, and ROC curves, with a focus on practical tools in scikit-learn.
- Regression Errors: Presented a framework for understanding errors in parametric regression, including bias-variance decomposition and regularization techniques.
- Decision Trees: Outlined the motivation, advantages, and mechanics of decision trees, including the ID3 algorithm and pruning techniques.
- Linear Regression: Reviewed key concepts of linear regression, including model formulation, cost function, and implementation in scikit-learn.
Achievements
- Gained insights into the selection and optimization of model evaluation metrics.
- Enhanced understanding of regression error frameworks and decision tree algorithms.
Pending Tasks
- Further exploration of advanced classification techniques and their practical applications.
- Continued study of regression model improvements and decision tree optimizations.