How is “model accuracy” defined in machine learning?

Model accuracy in machine learning is defined as the ratio of correct predictions to total observations. This metric provides a straightforward measure of how well a model is performing in terms of its predictive capabilities. When the model makes a prediction, it can either be correct or incorrect, and by calculating this ratio, one can understand the proportion of accurate predictions relative to the total number of predictions made.

This concept is fundamental in evaluating the performance of classification models. A higher accuracy indicates that the model excels at making predictions that align with the actual outcomes, while a lower accuracy suggests that the model may require further tuning or a different approach to improve its predictive power. Accurately measuring model performance using this metric helps in comparing different models and making informed decisions about which one to deploy in practical applications.