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
>>> accuracy = overall_accuracy(df, labels, y_true)
>>> print(accuracy)
0.47619047619047616
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.
Updated 9 months ago