This interview question was actually asked during an interview for a data science position at Google. Although simple, it shows how important solid fundamentals are for ML/AI jobs nowadays. First, what is a confusion matrix? It’s a 4 cell matrix used for binary classification, with Prediction true/false as columns, and actual true/false as rows. We denote these as TP, FN, FP, and TN. Precision is the ratio of true positives to predicted positives (factoring in FP, TP + FP). We optimize for precision if false positives are costly (spam). Recall is the ratio of true positives to actual positives (factoring in FN, TP + FN). We optimize for recall when false negatives are costly. F1 Score is the harmonic mean of both. it’s = 2 · (Precision · Recall) / (Precision + Recall). It balances both. These metrics are more descriptive than accuracy, and are used as additional metric to prevent being misled. This is especially true for imbalanced datasets where a model can achieve high accuracy while still performing poorly on the class that matters most. Learn AI, simply. Join 9000+ Others in our Visual AI Newsletter. Get your weekly research breakdown (link in bio 🔗). #datascience #machinelearning #deeplearning #coding #math - @aibutsimple