Classification Report Explained — Precision, Recall, Accuracy, Macro average, and Weighted Average

By Chanaka

Why do we need a classification report?

When we’re using a classification model to predict things like whether an email is spam or not, we need to know how well it’s doing. That’s where a classification report comes in handy. It gives us a detailed summary of how our model is performing.

Think of it like getting a report card in school. Just like a report card tells you how well you’re doing in different subjects, a classification report tells us how well our model is doing in different aspects of classification.

In the classification report, we can see things like accuracy, which tells us overall how often our model is correct. We also see precision, recall, and F1 Score, which give us insights into how well our model is doing at correctly identifying different classes (like spam and non-spam emails).

By looking at these metrics in the classification report, we can understand if our model is doing a good job on new data that it hasn’t seen before. It’s like checking our model’s performance to make sure it’s ready to tackle real-world tasks effectively.

Rather than the classification report, we have bunch of ways to evaluate the classification models. Ex — Accuracy, ROC and AUC…