Evaluate the use of a source indicator for resolving inconsistencies in a multi-city housing model.
Case context: You are developing a housing-price prediction model using training data collected from two different cities, Detroit and New York City. The pricing structures in these cities are highly inconsistent, meaning similar houses have wildly different prices depending on the city. You are considering adding an extra feature to each training example indicating the city of the property.
Question: Based on the source material, evaluate what effect this feature will have on the target variable y, and decide whether this is a commonly adopted solution in machine learning practice.
Sample answer: Adding the city as a source indicator feature to the input x will make the target value y unambiguous, resolving the inconsistency between Detroit and New York City data. However, you should be aware that this specific approach is not frequently used in practice.
Key points:
- Adding a city indicator to input x makes the target value y unambiguous.
- The feature addresses inconsistencies between Detroit and New York City data.
- Despite its theoretical clarity, this approach is not frequently used in practice.
Rubric: The evaluation must state that the target value becomes unambiguous when the city is specified in input x, and acknowledge that this approach is not frequently used in practice.
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What is the primary effect of adding a city indicator feature to training examples that mix Detroit and New York City housing data?
True or False: Andrew Ng reports that adding a source indicator feature to resolve inconsistent auxiliary data is a frequently used technique in practice.
When a city indicator is added as a feature to each training example, the target value y becomes _____ given the input x.
What can be added to each training example to resolve inconsistencies when data comes from multiple sources?
Adding a source indicator feature (e.g., city) to input x makes the target value y unambiguous.
Given an input x that specifies the city, the target value of _____ is now unambiguous.
Match each concept in the source indicator approach to its correct description.
Order the steps to apply the source indicator feature approach to inconsistent training data.
How does Andrew Ng characterize the practical adoption of the source indicator feature approach for inconsistent data?
Andrew Ng presents the source indicator feature approach as a frequently used best practice for handling inconsistent data.
In the housing-price example, adding a feature indicating the _____ to each training example can resolve inconsistencies across data sources.
Match each scenario to the role it plays in the source indicator feature approach.
Order the reasoning steps Andrew Ng uses to introduce and assess the source indicator feature approach.
Analyze the efficacy and practical limitations of adding a source indicator feature to resolve inconsistent auxiliary data.
Evaluate the use of a source indicator for resolving inconsistencies in a multi-city housing model.
Explain the theoretical effect of specifying the city in a housing-price input when combining inconsistent data sources.