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An auxiliary data source is inconsistent with the target task when the same input features can imply different labels depending on the data source. If one only wants to predict New York City housing prices, Detroit housing data is inconsistent because the same house size can have a very different price in the two cities, so mixing the datasets would hurt performance.

Inconsistent Auxiliary Data Source

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One way to address inconsistent data from multiple sources is to add a feature indicating the source, such as the city in a housing-price example. Once the input specifies the city, the target value becomes unambiguous, although this approach is not frequently used in practice in the cited discussion.

Adding a Source Indicator Feature for Inconsistent Data

Effect of mixing inconsistent Detroit housing data when predicting NYC prices

Consistency of housing price data between NYC and Detroit

Handling _____ auxiliary data in target task training

Terms related to inconsistent auxiliary data sources

Decision process for evaluating auxiliary data consistency

When is an auxiliary data source inconsistent with the target task?

Performance impact of mixing inconsistent datasets

Relative pricing of Detroit housing compared to _____ prices

Matching scenarios with their consistency classification

Sequence explaining why mixing Detroit and NYC data hurts performance

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