# Classifier calibration

Most statistical, machine learning, and deep learning models output predicted class labels, and the models are typically evaluated in terms of their accuracy.

Accuracy is a prevalent measure for assessing the performance of a machine learning classification model. It quantifies the ratio of instances that are correctly identified to the overall count in the dataset. In other words, accuracy tells us how often the model’s predictions align with the true labels of the data.

The accuracy score measures how often the model’s predictions match the true observed labels. It is calculated as the fraction of correct predictions out of all predictions made. Accuracy scores between 0 and 1 quantify how accurate the model’s predictions are compared to the ground truth data. A higher accuracy score close to 1 signifies that the model is performing very accurately overall, with most of its predictions being correct. A lower accuracy approaching 0...