Overview of Conformal Prediction

In today’s world, where data plays a crucial role in decision making, it has become increasingly important to measure uncertainty in predictions. To achieve this, a relatively new framework called conformal prediction is gaining popularity. This framework provides probabilistic predictions that are not only robust and reliable but also trustworthy. It is a powerful tool that offers measures of confidence, accuracy, and reliability for a given prediction, allowing users to make informed choices with more certainty.

This chapter will provide an overview of conformal prediction.

It will explain why conformal prediction is a valuable tool for quantifying the uncertainty of predictions, especially in critical settings such as healthcare, self-driving cars, and finance. We will also discuss the concept of uncertainty quantification (UQ) and how the conformal prediction framework has successfully addressed the challenge of quantifying uncertainty...

Understanding uncertainty quantification

Uncertainty is an inherent part of any prediction, as there are always factors that are unknown or difficult to measure. Predictions are typically made based on incomplete data or models that are unable to capture the full complexity of the real world. As a result, the predictions are subject to various sources of uncertainty, including randomness, bias, and model errors.

To mitigate the risks associated with uncertainty, it is essential to quantify it accurately. By quantifying uncertainty, we can estimate the range of possible outcomes and assess the degree of confidence we can have in our predictions. This information can be used to make informed decisions and to identify areas where further research or data collection is needed.

UQ is a field of study that helps us measure how much we don’t know when we make predictions. UQ tries to estimate the probability of outcomes even if some aspects of the system under study are not known...

Different ways to quantify uncertainty

There are several different approaches to quantify uncertainty, each with its own strengths and limitations. Here are a few examples:

• Statistical methods: Statistical methods are widely used for UQ and involve using probability distributions to model the uncertainty in data and predictions. These methods are widely used in fields such as finance, engineering, and physics and involve tools such as confidence intervals, regression analysis, Monte Carlo simulations and hypothesis testing.
• Bayesian methods: Bayesian methods involve using prior knowledge and data to update our beliefs about the uncertainty in predictions. These methods are widely used in machine learning, natural language processing, and image processing. Bayesian tools include Bayesian inference – statistical methods to update beliefs about the uncertainty of predictions based on new data – and Bayesian networks – graphical models that represent probability...

Quantifying uncertainty using conformal prediction

Quantifying the uncertainty of machine learning predictions is becoming increasingly important as machine learning is used more widely in critical applications such as healthcare, finance, and self-driving cars. In these applications, the consequences of incorrect predictions can be severe, making it essential to understand the uncertainty associated with each prediction.

For example, in healthcare, machine learning models are used to make predictions about patient outcomes, such as the likelihood of a disease or the effectiveness of a treatment. These predictions can have a significant impact on patient care and treatment decisions. However, if the model is unable to produce an estimate of its own confidence, it may not be useful and could potentially be risky to rely upon.

In contrast, if the model can provide a measure of its own uncertainty, clinicians can use this information to make more informed decisions about patient...

Summary

In this chapter, we have provided an overview of conformal prediction and explained why conformal prediction is a valuable tool for quantifying the uncertainty of predictions, especially in critical settings such as healthcare, self-driving cars, and finance. We also discussed the concept of UQ and how the conformal prediction framework has successfully addressed the challenge of quantifying uncertainty.

In the next chapter, we will dive deeper into the fundamentals of conformal prediction and apply it to binary classification problems. We will illustrate how you can apply conformal prediction to your own binary classification problems by computing non conformity scores and p-values and then using the p-values to decide which class labels should be included in your prediction sets.