Kovariáteltolódások

Kovariáteltolódások, also known as covariate shifts, refer to a phenomenon in machine learning and statistics where the distribution of the input features (covariates) changes between the training and testing or deployment phases of a model. This discrepancy can significantly degrade the performance of a model, as it was trained on data with a different underlying statistical properties than it encounters in practice.

There are several types of covariate shift. *Covariate shift* specifically implies that the conditional distribution of the target variable given the input features remains the same, but the marginal distribution of the input features changes. For example, a spam filter trained on emails from 2010 might perform poorly on emails from 2023 because the language, topics, and common phrases have evolved, even if the underlying characteristics that define spam haven't fundamentally changed.

Another related concept is *concept drift*, where the relationship between the input features and the target variable itself changes over time. This is distinct from covariate shift, although both can lead to model performance degradation.

Detecting and addressing covariate shift is crucial for building robust and reliable machine learning systems. Common detection methods involve statistical tests to compare the distributions of training and testing data, or monitoring model performance over time. Strategies for mitigation include re-weighting training samples to better match the target distribution, collecting new labeled data from the deployment environment, or using domain adaptation techniques. Ignoring covariate shift can lead to models that make inaccurate predictions in real-world scenarios.