βββββββββββββββββββββββ The bias-variance tradeoff is a central problem in supervised learning. Ideally, one wants to choose a model that both accurately capture the regularities in its training data, but also generalizes well to unseen data. Unfortunately, it is typically impossible to do both simultaneously. High-variance learning methods may be able to represent their training set well but are at risk of overfitting to noisy or unrepresentative training data. In contrast, algorithms with high bias typically produce simpler models that don't tend to overfit but may underfit their training data, failing to capture important regularities. Models with low bias are usually more complex (e.g. higher-order regression polynomials), enabling them to represent the training set more accurately. In the process, however, they may also represent a large noise component in the training set, making their predictions less accurate - despite their added complexity. In contrast, models with higher bias tend to be relatively simple (low-order or even linear regression polynomials) but may produce lower variance predictions when applied beyond the training set. ββββββββββββββββββββββββ - #data #datascience #datascientist #datavisualization #dataviz #machinelearning #artificialintelligence #machinelearningalgorithms #algorithm #engineering #engineer #math #mathematics #statistics #studygram #learn #study #visualization #simulation #science #mathconcepts #datascienceweekend #bias #variance #tradeoff #error #estimationerror (at United States)
