📅 2025-06-24 — Session: Analyzed classification and regression model metrics
🕒 16:40–17:00
🏷️ Labels: Classification, Regression, Metrics, Machine Learning, Scikit-Learn, Data Science
📂 Project: Teaching
⭐ Priority: MEDIUM
Session Goal:
The session aimed to explore and reflect on various metrics and methodologies for evaluating classification and regression models within data science and machine learning contexts.
Key Activities:
- Discussed the importance of selecting appropriate metrics for classification models, such as confusion matrices, precision, recall, F1, and ROC curves, emphasizing metrics that reflect real risk over mere accuracy.
- Explored errors in parametric regression, including relative squared error, bias-variance decomposition, polynomial examples, cross-validation, regularization, and model combinations.
- Outlined decision tree concepts, including the ID3 algorithm, overfitting, pruning, handling continuous variables, computational complexity, and comparisons between univariate and multivariate splits.
- Reviewed classification techniques using the MNIST dataset, focusing on binary classification, model training, evaluation metrics, and comparisons between classifiers.
- Covered linear regression model concepts, including model formulation, cost function, training methods, and implementation in Scikit-Learn.
Achievements:
- Gained insights into optimizing classification metrics using practical tools in scikit-learn.
- Developed a comprehensive understanding of regression errors and techniques to improve model accuracy.
- Clarified decision tree mechanics and their application in machine learning.
Pending Tasks:
- Further exploration of advanced classification techniques and their applications in real-world scenarios.
- Implementation of discussed methodologies in practical projects to validate theoretical insights.