Understanding Model Evaluation Metrics: Accuracy, Precision, Recall and F1 Score

Confusion Matrix: A table used to visualize the performance of a classification model.

True Positive(TP): Correctly predicted positive instances.

True Negative(TN): Correctly predicted negative instances.

False Positive(FP): Incorrectly predicted positive instances (Type I error).

False Negative(FN):Incorrectly predicted negative instances (Type II error).

Accuracy: The proportion of correctly classified instances out of the total instances.

Accuracy = (TP + TN) / (TP + TN + FP + FN).

Precision: Out of all instances predicted as positive, how many are actually positive.

Precision = TP / (TP + FP).

Recall: Out of all actual positive instances, how many were correctly predicted as positive.

Recall = TP / (TP + FN).

F1 Score: The harmonic mean of precision and recall. Provides a balanced measure when there is an uneven class distribution.

F1 = 2* (Precision Recall) / (Precision + Recall).

points to ponder before selecting your best model

1. Accuracy measures the overall correctness of a model, while precision focuses on the proportion of correctly predicted positives out of all instances predicted as positive. Accuracy considers both true positives and true negatives, while precision is more concerned with minimizing false positives.
2. Accuracy can be a sufficient metric when the dataset is balanced and the costs associated with false positives and false negatives are roughly equal. This implies that both types of errors are equally important.
3. Accuracy alone can be misleading, especially with imbalanced datasets or when the costs of different types of errors are unequal. Metrics like precision, recall, and F1 score provide a more nuanced understanding of model performance in such cases.
4. Precision and recall have an inverse relationship. Increasing precision often comes at the cost of decreasing recall, and vice versa. This is because focusing on minimizing one type of error can lead to an increase in the other.
5. In medical diagnosis, prioritizing recall is crucial to minimize false negatives. A high recall ensures that most sick patients are correctly identified, even if it leads to some healthy individuals being misclassified as sick. This is important because missing a diagnosis can have severe consequences.
6. In spam detection, high precision is more desirable than high recall. It is more important to ensure that legitimate emails are not flagged as spam, even if it means missing a few actual spam emails. A high precision minimizes the inconvenience caused to users by false positives.
7. The trade-off between precision and recall refers to the inherent tension between minimizing false positives and minimizing false negatives. Depending on the application, prioritizing one over the other may be necessary, but finding a balance between the two is often desirable.
8. A high F1 score indicates a balance between precision and recall. It suggests that the model performs well in terms of both minimizing false positives and false negatives. This is particularly valuable when dealing with imbalanced datasets.