Machine Learning: No Fluff, No Jargon, Just the Real Deal — 3) Conformal Prediction: A Robust Approach to Reliable Machine Learning
In the ever-evolving field of machine learning, making accurate and reliable predictions is paramount. Traditional methods often rely on strong assumptions about data distribution, which can limit their applicability in real-world scenarios. Conformal prediction is a powerful technique that offers a more flexible and robust approach, ensuring that predictions are not only accurate but also come with a quantifiable level of confidence.
Assumptions in Machine Learning
Machine learning models are built on certain assumptions about the data they are trained on. These assumptions play a critical role in how the models perform in practice. Traditionally, statistical methods rely heavily on parametric assumptions, such as the data following a Gaussian distribution. In contrast, many machine learning techniques operate under the assumption of independently and identically distributed (IID) data. Conformal prediction is particularly valuable because it does not require these strong assumptions, making it more adaptable to a wider range of problems.
Understanding Conformal Prediction
