Model Accuracy is a measure of how correctly a machine learning model performs its intended prediction or classification task. It is commonly calculated by comparing the number of correct predictions with the total number of predictions made by the model. A higher accuracy generally indicates that the model is making correct predictions more frequently, although accuracy alone may not fully describe performance when datasets are imbalanced.
For example, an image classification model designed to identify defective products can be evaluated by comparing its predictions with the actual inspection results. If the model correctly classifies 950 out of 1,000 products, its accuracy is 95%. Model Accuracy is useful for evaluating and comparing machine learning models, but other metrics such as precision, recall, F1 score, and mean absolute error may be more appropriate depending on the type of problem and the consequences of incorrect predictions.
