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Module 4: Evaluation Metrics for Classification — Machine Learning Zoomcamp

Lessons, materials, and key concepts for module 4 of Machine Learning Zoomcamp.

Machine Learning Zoomcamp > Module 4: Evaluation Metrics for Classification

4. Evaluation Metrics for Classification

• 4.1 Evaluation metrics: session overview
• 4.2 Accuracy and dummy model
• 4.3 Confusion table
• 4.4 Precision and Recall
• 4.5 ROC Curves
• 4.6 ROC AUC
• 4.7 Cross-Validation
• 4.8 Summary
• 4.9 Explore more
• 4.10 Homework

Community notes

Did you take notes? You can share them here (or in each unit separately)

• Some cross-validation methods
• [Notes from Kwang Yang](https://www.kaggle.com/kwangyangchia/notebook-for-lesson-4-m

Lessons

Key concepts

• Classification Metrics
• LLM Evaluation
• scikit-learn
• Jupyter Notebooks
• Trading Strategy

Homework and deadlines live in the course repository: machine-learning-zoomcamp.

Other modules in this course

• Module 1: Introduction to Machine Learning
• Module 2: Machine Learning for Regression
• Module 3: Machine Learning for Classification
• Module 5: Deploying Machine Learning Models
• Module 6: Decision Trees and Ensemble Learning
• Module 8: Neural Networks and Deep Learning
• Module 9: Serverless Deep Learning
• Module 10: Kubernetes and TensorFlow Serving