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Why a location indicator can resolve mixed housing-price data
Question: Suppose a training set mixes home-sale records from two cities with different price levels and you add a city label to each feature vector. What is the theoretical effect on the target value y?
Sample answer: The city label conditions the model on the data source, so the same house features no longer point to one ambiguous price. For a fixed x, y becomes well-defined because the model can tell which city generated the example.
Key points:
- The city label identifies which data source produced the example.
- Once the source is identified, the mapping from x to y is no longer ambiguous.
- The model can learn different relationships for different cities.
Rubric: The answer must state that the city label removes ambiguity by distinguishing the source so y is well-defined for a given x.
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Why a location indicator can resolve mixed housing-price data