Mastering Python for Finance - Second Edition

Accuracy score

An accuracy score is the ratio of correct predictions to the total number of observations. By default, it is expressed as a fractional value between 0 and 1. When the accuracy score is 1.0, it means that the entire set of predicted labels in the sample matches with the true set of labels. The accuracy score can be written as follows:

Here, I(x) is the indicator function that returns 1 for a correct prediction, and 0 otherwise. The accuracy_score function of the sklearn.metrics module calculates this score for us with the following code:

In [ ]:    from sklearn.metrics import accuracy_score print('accuracy_score:', accuracy_score(actual, ...