Analyzed classification and regression model metrics

• Day: 2025-06-24
• Time: 16:40 to 17:00
• Project: Teaching
• Workspace: WP 1: Strategic / Growth & Development
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Classification, Regression, Metrics, Machine Learning, Scikit-Learn, Data Science

Description

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.

Evidence

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