Data Science and Machine Learning

Currently Playing: Ep 23 — Machine Learning Precision and Recall - How Many, and How Sure (Precision & Recall)

#machinelearning #datascience #artificialintelligence #ai #ml #math #algorithms Core Idea: Two different questions about the same set of predictions. Concept: Precision and recall. Why it matters: Optimizing one often costs you the other. Core mechanic: You will demonstrate precision_score(y_true, y_pred) and recall_score(y_true, y_pred). Example: A spam filter — high precision (few false alarms) vs. high recall (catches everything). Common mistake: Reporting only overall accuracy on an imbalanced problem. The Rule: "Accuse too many, lose credibility. Miss too many, the point is moot." Next Clue: One number that can't be gamed by counting too much or too little (bridging to Ep 24: F1 Score).