Q: 11
[Exploratory Data Analysis]
A machine learning (ML) engineer is preparing a dataset for a classification model. The ML engineer
notices that some continuous numeric features have a significantly greater value than most other
features. A business expert explains that the features are independently informative and that the
dataset is representative of the target distribution.
After training, the model's inferences accuracy is lower than expected.
Which preprocessing technique will result in the GREATEST increase of the model's inference
accuracy?
Options
Discussion
Pretty sure it's A. Normalizing those features keeps their info but gets rid of scale problems. Makes sense in this scenario.
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