Confusion Matrix for Classification Models

Classification models are supervised ML models used to classify information into various classes - e.g. binary classification (true/false) or multi-class classification (facebook/twitter/whatsapp)

When it comes to classification models, we need a better metric than accuracy for evaluating the holistic performance of the model. The following article gives an excellent overview of Confusion Matrix and how it can be used to evaluate classification models (and also tune their performance).

https://www.analyticsvidhya.com/blog/2020/04/confusion-matrix-machine-learning/

Some snippets from the above article:

A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. This gives us a holistic view of how well our classification model is performing and what kinds of errors it is making.

A binary classification model will have false positives (aka Type 1 error) and false negatives (aka Type 2 error). A good example would be a ML model that predicts whether a person has COVID based on symptoms and the confusion matrix would look something like the below. 

Based on the confusion matrix, we can calculate other metrics such as 'Precision' and 'Recall'. 

Precision is a useful metric in cases where False Positive is a higher concern than False Negatives.

Recall is a useful metric in cases where False Negative trumps False Positive.

Recall is important in medical cases where it doesn’t matter whether we raise a false alarm but the actual positive cases should not go undetected!

F1-score is a harmonic mean of Precision and Recall, and so it gives a combined idea about these two metrics. It is maximum when Precision is equal to Recall.

Another illustration of a multi-class classification confusion matrix that predicts the social media channel.