Model Accuracy

Model accuracy is a fundamental evaluation metric in machine learning and statistics that measures the proportion of correct predictions made by a model out of all predictions. It is typically expressed as a percentage or decimal, calculated as (Number of Correct Predictions) / (Total Predictions). While simple and intuitive, it can be misleading for imbalanced datasets where other metrics like precision, recall, or F1-score are often more appropriate.

Also known as: Accuracy, Classification Accuracy, Prediction Accuracy, Acc, Correctness Rate

Why learn Model Accuracy?

Developers should learn about model accuracy to assess the performance of classification models, especially in balanced datasets where classes are equally represented. It is commonly used in initial model evaluation, educational contexts, and when stakeholders require an easily interpretable metric. However, it should be complemented with other metrics in real-world applications to avoid pitfalls like ignoring false positives/negatives.

Learning Resources

• docs: Scikit-learn Documentation: Model Evaluation
• tutorial: Towards Data Science: Accuracy, Precision, Recall, F1
• course: Coursera: Machine Learning by Andrew Ng
• video: YouTube: StatQuest with Josh Starmer - Accuracy, Precision, Recall