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Explore more — Machine Learning Zoomcamp Module 4

Check the precision and recall of the dummy classifier that always predict 'FALSE' F1 score = 2 P R / (P + R) Evaluate precision and recall at different thresholds, plot P vs R - this way you'll get the precision/recall curve (similar to...

Machine Learning Zoomcamp › Module 4: Evaluation Metrics for Classification › Explore more

Notes

Explore more

• Check the precision and recall of the dummy classifier that always predict “FALSE”
• F1 score = 2 P R / (P + R)
• Evaluate precision and recall at different thresholds, plot P vs R - this way you’ll get the precision/recall curve (similar to ROC curve)
• Area under the PR curve is also a useful metric

Other projects

• Calculate the metrics for the suggested datasets from the previous week

Key concepts

• Classification Metrics

Related notes

• 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-evaluation-metrics-session-overview
• 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

• Lesson file