Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

by Chip Huyen

Binary versus multiclass classification Within classification problems, the fewer classes there are to classify, the simpler the problem is. The simplest is binary classification, where there are only two possible classes. Examples of binary classification include classifying whether a comment is toxic, whether a lung scan shows signs of cancer, whether a transaction is fraudulent. It’s unclear whether this type of problem is common in the industry because they are common in nature or simply because ML practitioners are most comfortable handling them. When there are more than two classes, the problem becomes multiclass classification. Dealing with binary classification problems is much easier than dealing with multiclass problems. For example, calculating F1 and visualizing confusion matrices are a lot more intuitive when there are only two classes. When the number of classes is high, such as disease diagnosis where the number of diseases can go up to thousands or product classifications where the number of products can go up to tens of thousands, we say the classification task has high cardinality. High cardinality problems can be very challenging. The first challenge is in data collection.