Why does adding features not necessarily hurt performance if variance rises?
Question: In one to three sentences, explain what the source says should be done if adding more features increases variance.
Sample answer: If adding more features increases variance, the source says to apply regularization, which will usually eliminate that increase in variance.
Key points:
- Adding features can increase variance
- Regularization is the recommended response
- Regularization usually eliminates the variance increase
Rubric: Full credit for stating that regularization is the recommended response and that it usually removes the variance increase. Partial credit for only mentioning regularization without the expected outcome.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Tags
Data Science
D2L
Dive into Deep Learning @ D2L
Bayesian Statistics
Statistics
Machine Learning
Deep Learning
Supervised Learning
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
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Diagnose the right next step for a team hesitant to grow their regularized model.
Why does adding features not necessarily hurt performance if variance rises?
Which regularization method does the source specifically mention for deep learning models?
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