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# Concept Drift

Concept Drift is a situation in which the statistical properties of a target variable (what the model is trying to predict) changes over time in unforeseen ways.

Visually, we can say that a concept is a way to separate between the blue and green dots in the plot above. The black line represents a concept that separates the blue and green dots.

Types of Drifts in Machine Learning

For the following definitions let’s denote the following parameters:

X- Model’s input population.

ŷ – Model’s prediction.

Y- True label population.

Concept drift:  a change in the distribution of p(Y |X), meaning that there was a change in the relationship between the input of the model and the true label.

Prediction drift: a change in the distribution of the predicted label – p(ŷ |X), meaning that there was a change in the relationship between the input of the model and the model’s prediction.

Label drift: a change in the probability of a label p(Y).

Feature drift: a change in the probability of p(X), meaning there was a change in the distribution of the model’s input.

In order to better understand the effects of concept drift, we need to distinguish between two types of concept drift:

Virtual drift:  when p(X) changes but p(Y|X)does not change. Meaning that there was a change in the features’ underlying distribution, but the model’s performance hasn’t changed.

Real drift: There was a change in p(Y|X), meaning the performance of the model changed.

Virtual drift vs real drift is illustrated in the following figure.