Unlocking the Power of Assumptions in Machine Learning
Machine learning models are like intricate puzzles, pieced together based on assumptions about the data they are trained on. These assumptions are the guiding principles that influence the performance of the models in real-world scenarios. While traditional statistical methods often hinge on parametric assumptions, such as data conforming to a Gaussian distribution, machine learning takes a different approach. Many machine learning techniques operate under the assumption of independently and identically distributed (IID) data, allowing for more flexibility and adaptability in handling various types of information. One particularly noteworthy approach that stands out is conformal prediction, which breaks free from the shackles of strong assumptions, paving the way for a more versatile and robust predictive model.
Delving Deeper into Conformal Prediction