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**Balanced accuracy** is a metric we can use to assess the performance of a classification model.

It is calculated as:

**Balanced accuracy** = (Sensitivity + Specificity) / 2

where:

**Sensitivity**: The “true positive rate” – the percentage of positive cases the model is able to detect.**Specificity**: The “true negative rate” – the percentage of negative cases the model is able to detect.

This metric is particularly useful when the two classes are imbalanced – that is, one class appears much more than the other.

For example, suppose a sports analyst uses a logistic regression model to predict whether or not 400 different college basketball players get drafted into the NBA.

The following confusion matrix summarizes the predictions made by the model:

To calculate the balanced accuracy of the model, we’ll first calculate the sensitivity and specificity:

**Sensitivity**: The “true positive rate” = 15 / (15 + 5) = 0.75**Specificity**: The “true negative rate” = 375 / (375 + 5) = 0.9868

We can then calculate the balanced accuracy as:

- Balanced accuracy = (Sensitivity + Specificity) / 2
- Balanced accuracy = (0.75 + 9868) / 2
- Balanced accuracy = 0.8684

The balanced accuracy for the model turns out to be **0.8684**.

The following example shows how to calculate the balanced accuracy for this exact scenario using the **balanced_accuracy_score()** function from the **sklearn** library in Python.

**Example: Calculating Balanced Accuracy in Python**

The following code shows how to define an array of predicted classes and an array of actual classes, then calculate the balanced accuracy of a model in Python:

import numpy as np from sklearn.metrics import balanced_accuracy_score #define array of actual classes actual = np.repeat([1, 0], repeats=[20, 380]) #define array of predicted classes pred = np.repeat([1, 0, 1, 0], repeats=[15, 5, 5, 375]) #calculate balanced accuracy score balanced_accuracy_score(actual, pred) 0.868421052631579

The balanced accuracy is **0.8684**. This matches the value that we calculated earlier by hand.

**Note**: You can find the complete documentation for the **balanced_accuracy_score()** function here.

**Additional Resources**

An Introduction to Balanced Accuracy

How to Perform Logistic Regression in Python

How to Calculate F1 Score in Python