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Evaluation metrics: session overview — Machine Learning Zoomcamp Module 4

The fourth week of Machine Learning Zoomcamp is about different metrics to evaluate a binary classifier. These measures include accuracy, confusion table, precision, recall, ROC curves(TPR, FRP, random model, and ideal model), AUROC, and...

Machine Learning Zoomcamp › Module 4: Evaluation Metrics for Classification › Evaluation metrics: session overview

Notes

The fourth week of Machine Learning Zoomcamp is about different metrics to evaluate a binary classifier. These measures include accuracy, confusion table, precision, recall, ROC curves(TPR, FRP, random model, and ideal model), AUROC, and cross-validation.

For this project, we used a Kaggle dataset about churn prediction.

Add notes from the video (PRs are welcome)

• Notes from Peter Ernicke

Key concepts

• Classification Metrics
• LLM Evaluation

Related notes

• mlz-m01-crisp-dm
• mlz-m01-summary
• mlz-m02-car-price-prediction-project
• mlz-m02-computing-rmse-on-validation-data
• mlz-m02-feature-engineering
• mlz-m02-root-mean-squared-error-rmse
• mlz-m02-using-the-model
• mlz-m04-accuracy-and-dummy-model
• mlz-m04-confusion-table
• mlz-m04-cross-validation
• mlz-m04-explore-more
• mlz-m04-precision-and-recall
• mlz-m04-roc-auc
• mlz-m04-roc-curves
• mlz-m04-summary
• mlz-m06-decision-trees-parameter-tuning
• mlz-m06-selecting-the-best-model
• mlz-m06-xgboost-parameter-tuning

Sources

• Video
• Lesson file