Fundamentals of Conformal Prediction

This chapter will dive into conformal prediction, a powerful and versatile probabilistic prediction framework. Conformal prediction allows for effective quantification of uncertainty in machine learning applications. By learning and utilizing conformal prediction techniques, you will be able to make more informed decisions and manage risks associated with data-driven solutions more effectively.

This chapter will cover the mathematical underpinnings of conformal prediction. You will learn how to accurately measure the uncertainty that comes with your predictions. You will also become familiar with nonconformity measures, grasp the idea of prediction sets, and be able to evaluate your model’s performance in a thorough and meaningful manner. The abilities you will acquire through this chapter will be highly valuable in various academic and industrial fields where comprehending the uncertainty associated with predictions is essential.

In...

Fundamentals of conformal prediction

In this section, we will cover the fundamentals of conformal prediction. There are two variants of conformal prediction – inductive conformal prediction (ICP) and transductive conformal prediction (TCP). We will discuss the benefits of the conformal prediction framework and learn about the basic components of conformal predictors and the different types of nonconformity measures. We will also learn how to use nonconformity measures to create probabilistic prediction sets in classification tasks.

Definition and principles

Conformal prediction is a machine learning framework that quantifies uncertainty to produce probabilistic predictions. These predictions can be prediction sets for classification tasks or prediction intervals for regression tasks. Conformal prediction has significant advantages in equipping statistical, machine learning, and deep learning models with valuable additional features that instill confidence in their predictions...

Basic components of a conformal predictor

We will now look at the basic components of a conformal predictor:

• Nonconformity measure: The nonconformity measure is a function that evaluates how much a new data point differs from the existing data points. It compares the new observation to either the entire dataset (in the full transductive version of conformal prediction) or the calibration set (in the most popular variant – ICP. The selection of the nonconformity measure is based on a particular machine learning task, such as classification, regression, or time series forecasting, as well as the underlying model. This chapter will examine several nonconformity measures suitable for classification and regression tasks.
• Calibration set: The calibration set is a portion of the dataset used to calculate nonconformity scores for the known data points. These scores are a reference for establishing prediction intervals or regions for new test data points. The calibration...

Summary

In this chapter, we have deep-dived into the fundamentals and mathematical foundations of conformal prediction, a powerful and versatile probabilistic prediction framework. We have learned about different measures of nonconformity used in classification and regression, building solid foundations for applying conformal prediction to your industry applications.

In the next chapter, we’ll cover the concepts of validity and efficiency in the context of probabilistic prediction models, building upon the foundations laid in the previous chapters.