Category: Machine learning

True Negative, Explained

If you want to master building classification systems for machine learning, you need to understand how to evaluate classifiers. And in turn, that means you need to understand classification metrics....continue reading.

True Positive, Explained

When you begin immersing yourself in the world of classification systems, you’ll encounter a large number of different classification metrics: precision; recall; accuracy; sensitivity and specificity; F1-score; and many more....continue reading.

Confusion Matrix, Explained

The confusion matrix is an important and commonly used tool in machine learning. This is particularly true of classification problems, where we build systems that predict categorical values. Because they’re...continue reading.