Databricks
DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST · Question #64
DATABRICKS-CERTIFIED-PROFESSIONAL-DATA-SCIENTIST Question #64: Real Exam Question with Answer & Explanation
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Question
You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?
Exhibit
Options
- ALogistic regression with L1 regularization, to prevent overfitting
- BNaive Bayes, because Bayesian methods act as regularlizers
- Ck-nearest neighbors, because it uses local neighborhoods to classify examples
- DRandom forest because it is an ensemble method
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