# Categorical accuracy

The categorical accuracy metric measures how often the model gets the prediction right.

Categorical accuracy = 1, means the model’s predictions are perfect.

In a multiclass classification problem, we consider that a prediction is correct when the class with the highest score matches the class in the label.

The formula for categorical accuracy is:

$\begin{array}{rcl} \text{Accuracy} & = & \dfrac{\text{Number of correct predictions}}{\text{Total number of predictions}} \\ \end{array}$

## Suggestions on how to improve

### Large discrepancy

If there is a large discrepancy between training and validation accuracy (called overfitting), try to introduce dropout and/or batch normalization blocks to improve generalization. Overfitting means that the model performs well when it’s shown a training example (resulting in a low training loss), but badly when it’s shown a new example it hasn’t seen before (resulting in a high validation loss).

A large discrepancy can also show that the validation data are too different from the training data.

### Low accuracy

If the training accuracy is low, the model is not learning well enough. Try to build a new model or collect more training data.