Guide to the Intuitive Confusion Matrix - Analytics Vidhya

This article was published as a part of the Data Science Blogathon. Now, we enter the secret sauce: CM_Norm adjusts the colour-bar, such that its point of origin is equal to the accuracy expected for a random prediction. Essentially, the "naive-prediction accuracy" is our "point of origin" because a model which predicts worse than a coin-flip, is not a helpful model to begin with (hence the name: "coin-flip confusion-matrix"). In other words, we are interested in a models "excess performance", rather than its "absolute" error rates. To give two examples: For 3 different classes, the "point of origin", of the colour-bar, would be set at 1/3, or for 10 classes it would be set at 1/10.