# What does the color coding and normalized values in confusion matrix actually specify?

I am unable to infer anything about the model from the following confusion matrix. What is the color coding actually specifying?

For example, when predicted label is 1 and true label is 1, the value in the matrix at that point is 0.20. Does that mean its accuracy? Does it mean that the model is only able to predict 1 only 20% of times when the label is actually 1?

PS: SO URL for a more clear image

Each element_ab shows the probability of predicting label a (horizontal axis) when the true label is b (vertical axis) For example, when the true label is 0, it will be predicted as label 2 with the probability of 0.14
• Okay, so the model is not good at all? For true = pred , probabilities are close to ~0.2 – Amanda Apr 25 '19 at 8:12