Overall Accuracy

Overview

The Accuracy Score is a metric used to evaluate the accuracy of a classification model by comparing the predicted labels (df) with the true labels (y_true). It measures the proportion of correct predictions out of the total number of predictions.

Example

Python

>>> accuracy = overall_accuracy(df, labels, y_true)
>>> print(accuracy)
0.47619047619047616

Source

Interpretation

The overall accuracy indicates how accurately the model predicted the correct results. A normalized accuracy score of 1.0 means that the model correctly predicted every result compared with the true results while a score of 0.0 means the opposite.