What Is the Naive Classifier for Each Imbalanced Classification Metric?

Last Updated on August 27, 2020

A common mistake made by beginners is to apply machine learning algorithms to a problem without establishing a performance baseline.

A performance baseline provides a minimum score above which a model is considered to have skill on the dataset. It also provides a point of relative improvement for all models evaluated on the dataset. A baseline can be established using a naive classifier, such as predicting one class label for all examples in the test dataset.

Another common mistake made by beginners is using classification accuracy as a performance metric on problems that have an imbalanced class distribution. This can result in high accuracy scores even when the majority class is predicted for all cases. Instead, an alternate performance metric must be chosen among a suite of classification measures.

The challenge is that the baseline in performance is dependent upon the choice of performance metric. As such, deep knowledge of each performance metric may be required in order to select an appropriate naive classifier to establish a performance baseline.

In this tutorial, you will discover which naive classifier to use for each imbalanced classification performance metric.

After completing this tutorial, you
To finish reading, please visit source site