Scikit-learn: How to calculate the True Negative

When working with machine learning models, it is important to be able to evaluate their performance. One common metric used to assess the performance of a classification model is the True Negative (TN) rate. The True Negative rate measures the number of correctly predicted negative instances out of all actual negative instances.

In order to calculate the True Negative rate using the scikit-learn library in Python, you can use the following code snippet:

from sklearn.metrics import confusion_matrix

# Assuming y_true contains the true labels and y_pred contains the predicted labels
tn = confusion_matrix(y_true, y_pred)[0, 0]

This code snippet uses the confusion_matrix function from the sklearn.metrics module to calculate the True Negative rate. The confusion_matrix function returns a 2×2 matrix where the element in the top-left corner represents the True Negative value (TN).

By dividing the True Negative value by the total number of negative instances, you can calculate the True Negative rate (TN rate).

By calculating the True Negative rate, you can gain insight into how well your classification model is performing, particularly in correctly identifying negative instances.

Overall, understanding how to calculate and interpret the True Negative rate is crucial for evaluating the performance of your machine learning models.