What Plentiful Data Changes About Feature Selection
Question: How has abundant training data changed the way many deep learning practitioners handle feature selection?
Sample answer: When training data is plentiful, practitioners are less likely to spend time hand-picking features. Instead, they often supply a broad set of candidate features to the learning algorithm and let the model determine which ones are useful from the data.
Key points:
- Abundant data has reduced reliance on manual feature selection.
- Practitioners commonly provide many or all available features to the model.
- The algorithm then learns which features to use.
Rubric: The answer must explicitly describe the shift away from manual feature selection and explain that the model is typically given a broad feature set so it can decide what to use on its own.
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Related
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In many data-rich deep learning projects, people often provide _____ features to the learner and let it determine which ones matter most.
Match each feature-reduction case to its likely effect on bias.
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When is feature selection especially useful?
When training data is abundant for deep learning, teams often rely less on manual feature pruning and more on letting the model learn from a broad set of inputs.
A tenfold reduction in input features
Match each idea to the description that best fits feature selection and variance reduction.
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What Plentiful Data Changes About Feature Selection