Based on the “Machine Learning ” crash course from Google for Developers: https://developers.google.com/machine-learning/crash-courseIn this episode, we go beyond accuracy and dive into how to evaluate model performance across all thresholds using ROC curves and AUC. We also break down prediction bias—why your model might look accurate but still be off-target—and how to detect early signs of bias caused by data, features, or training bugs. Tune in to learn how to make more reliable and fair classification models!
Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.