Accuracy

Accuracy = (TP + FP) / (P + N)

Accuracy is a commonly used evaluation metric in classification tasks that measures the overall correctness of a model’s predictions. It provides a straightforward assessment of how well the model performs by calculating the ratio of correctly predicted instances to the total number of instances in the dataset.

Accuracy is easy to interpret and understand because it represents the proportion of correct predictions made by the model. However, it has limitations, especially in situations where the dataset is imbalanced or when misclassifying certain classes carries more significant consequences. Accuracy can be misleading when the classes in the dataset are imbalanced, meaning one class has significantly more instances than the others.

In such cases, a model that predicts the majority class for all instances would still achieve a high accuracy, but it may fail to perform well on the minority classes. Therefore, it is important to consider other evaluation metrics, especially when dealing with imbalanced datasets. Accuracy is a useful metric when misclassifying any class is equally important, and the class distribution is relatively balanced. For instance, in cases where the cost of false positives and false negatives is similar, accuracy provides a general overview of the model’s performance.

Classification evaluation metrics are quantitative measures used to assess the performance and accuracy of a classification model. These metrics provide insights into how well the model can classify instances into predefined classes or categories.

The commonly used classification evaluation metrics are: