Discover the table providing a detailed breakdown of classification performance, showing where models get things right and wrong.

Explore the graph showing binary classifier performance across different decision thresholds, plotting true positive rate against false positive rate.

Understand the harmonic mean of precision and recall, providing a single balanced metric especially useful for imbalanced datasets.

Learn about the fraction of actual positive cases correctly identified by a model, essential when missing positives has serious consequences.

Discover the fraction of positive predictions that are actually correct, crucial when the cost of false positives is high.

Bonus episode as I reflect on that we have hit 65 episodes.

Discover the centralized repository for storing, managing, and serving machine learning features across an organization.

Understand the series of processes that move and transform data from sources to destinations for analysis or ML.