Evaluation metrics are measures used to evaluate the performance of a model or algorithm on a given dataset. These metrics provide a way to assess the effectiveness of a model and can help determine which model is best suited for a given problem.

Two commonly used evaluation metrics are accuracy and F1 score.

Accuracy is the ratio of correct predictions made by a model to the total number of predictions made. It is a simple and intuitive metric that is often used as a measure of a model’s performance. For example, if a model predicts the correct label for 90 out of 100 examples, its accuracy would be 90%.

However, accuracy is not always the best metric to use for evaluating a model’s performance. This is because it does not take into account the imbalance in the distribution of classes in the dataset. For example, if a dataset has 95% of examples belonging to one class and only 5% belonging to the other class, a model that always predicts the majority class would have a high accuracy, even though it is not making any useful predictions.

In such cases, F1 score can be a better metric to use. It is the harmonic mean of precision and recall, and takes into account both the false positives and false negatives made by a model. Precision is the ratio of true positive predictions to all positive predictions, while recall is the ratio of true positive predictions to all actual positive examples.

For example, if a model makes 100 positive predictions, but only 80 of them are correct, its precision would be 80%. Similarly, if there are 100 positive examples in the dataset and the model only correctly predicts 80 of them, its recall would be 80%. The F1 score combines these two measures and ranges from 0 to 1, with a higher value indicating a better performing model.

In summary, evaluation metrics are important tools for evaluating the performance of a model or algorithm on a given dataset. They provide a way to assess the effectiveness of a model and can help determine which model is best suited for a given problem. Accuracy and F1 score are two commonly used evaluation metrics, with accuracy being a simple and intuitive measure of performance and F1 score taking into account the balance of classes in the dataset.