A custom classifier is a machine learning model trained to understand and sort unstructured data into predefined categories based on a specific learning objective. It helps humans in characterizing items and then sorting them into categories, making decisions about input data, and transforming raw data before it's fed into the classifier. Custom classifiers are useful in various fields such as object detection, sentiment analysis, audio classification, and more. They use different algorithms and architectures like Perceptron, Naive Bayes, decision tree, logistic regression, K-nearest neighbor, artificial neural networks, support vector machines, and others to achieve their learning objective. By using custom classifiers, engineering teams can build more quickly and efficiently, while also reducing the costs of managing and modifying a production pipeline for classification. The output of a custom classifier is usually an integer representing the predicted class, which is then mapped to the actual class based on a predefined mapping.