What metrics are best for evaluating classification models?

Evaluation of the performance is an important step in any machine-learning workflow. The right evaluation metrics depend on the type of problem, the class balance, and the goals of the project. To assess classification models, a variety of metrics are available. Each one provides unique insight into the performance of the model. Data Science Course in Pune

accuracy is a common metric that measures the percentage of instances correctly classified out of all instances. Although accuracy is widely used and easy to understand, it can be unreliable when dealing with datasets that are imbalanced. In a dataset with 95% of samples belonging to one class, for example, a model that predicts only the majority class would still have high accuracy even though it is not really effective.

precision recall and F1 score are often used to address the limitations in accuracy, especially when scenarios have imbalanced classes. Precision is the ratio between the number of true positives and the total positive predictions that the model made. It shows us how many positive predictions are correct. Recall is also called sensitivity, or the true positive rate. It's the ratio between the true positives and the total positives. It measures the model’s ability to recognize all relevant instances. The F1 score is the harmonic average of precision and recall. It provides a single metric which balances them both. This is especially useful when one wants to find the optimal balance between recall and precision.