Offset

TL;DR

An offset is an added error term used to correct systematic bias in a model’s predictions.
Common causes include unrepresentative training data, model complexity, and noise or outliers.
Offsets reduce prediction accuracy and are addressed by collecting more representative data or using techniques like data augmentation or regularization.

Definition

An offset is an error term that is added to the predicted output of a machine learning model to correct for the model’s bias or to account for certain variables that the model does not take into consideration.

Explanation

Offset refers to the bias or error introduced into a model’s predictions because of factors the model fails to capture. Factors that can contribute to offset include the quality of the training data, the complexity of the model, and the presence of noise or outliers in the data. Because real-world data are often complex and variable, models commonly exhibit some level of offset, which reduces prediction accuracy. Correcting offset typically requires more diverse and representative training data or techniques such as data augmentation or regularization.

Examples

Example 1: Predicting Housing Prices

Suppose we build a model to predict house prices using a dataset of houses in a neighborhood with features such as size, age, number of bedrooms, and sale price. If the training data only include houses that were recently sold and exclude houses that have been on the market longer, the model may underestimate prices for houses that have been on the market longer. Houses on the market longer may have lower sale prices, and the model would not have learned to account for that factor, producing an offset.

Example 2: Predicting Customer Churn

Suppose we build a model to predict which customers will churn using customer features like age, income, location, and past churn status. If the training data include only customers who have churned and exclude customers who remained loyal, the model may overestimate churn likelihood for all customers. Because the model did not learn that most customers do not churn, it will be more likely to predict churn, creating an offset.

Notes or pitfalls

Offset commonly arises when training data are not representative of the full population the model will encounter.
Other contributors to offset include model complexity and the presence of noise or outliers.
Offsets reduce prediction accuracy; mitigating them requires more diverse representative data or techniques such as data augmentation or regularization.

Bias
Error term
Training data
Data augmentation
Regularization
Outliers
Noise
Model complexity