# Distribution Shift Distribution shift is the broader concept that refers to any situation where the **training data** and **test data** come from different distributions. It's a fundamental challenge in machine learning because most algorithms assume training and test data are drawn from the same distribution. Most machine learning models assume training and test data are **i.i.d.** (independent and identically distributed). When this assumption breaks due to distribution shift, model performance can degrade significantly. Different types of shift require different adaptation strategies. ## Types of shifts **[[Covariate Shift]]** • P(X) changes, P(Y|X) stays the same • Input distribution shifts but conditional relationship is stable • Example: Medical model trained on young patients, tested on elderly patients > [!NOTE] > Covariate shift is often considered the "easiest" type to handle because the core relationships P(Y|X) remain valid - we just need to account for the different input distribution. **Label Shift** (also called Prior Probability Shift) • P(Y) changes, P(X|Y) stays the same • The proportion of different classes changes, but what each class "looks like" stays the same • Example: Fraud detection model trained when fraud rate was 1%, deployed when fraud rate becomes 5% **Concept Drift** • P(Y|X) changes over time • The fundamental relationship between inputs and outputs shifts • Example: Stock prediction model where economic relationships change after a market crash **Domain Shift** • More general case where multiple aspects change between domains • Could involve changes in P(X), P(Y), and P(Y|X) • Example: Image classifier trained on photos, tested on paintings