You are working as a machine learning specialist for a car rental firm that wishes to use machine learning to optimize the cost per mile of its rental cars based on geographic region. You have a large car rental database for use in your model training that has features such as rental car type, rental geographic region, miles driven, average regional gas price, etc. During your exploratory data analysis tasks, you notice that your feature seat contains outliers for the miles driven and average regional gas price. These outliers are likely to make your regression algorithm-based model unstable. What data preparation technique can you use to prepare your data for your model training?

Answer options:

A.Normalize your features to reduce the effect of the outlier data.
B.Use Quantile Binning of your features to reduce the effect of the outlier data.
C.Min-Max Scale your features to reduce the effect of the outlier data.
D.Standardize your features to reduce the effect of the outlier data.

Correct Answer: D
Option A is incorrect. Both normalization and standardization are types of data scaling that machine learning specialists use to prepare their data for training and inference. Normalization rescales your data so that all values range from 0 to 1. Therefore, normalization doesn’t handle outliers well.
Option B is incorrect. Quantile Binning is used to categorize feature values into bins. This technique would not reduce the effect of your outliers since the outliers would skew whichever bin in which they are placed.
Option C is incorrect. The Min-Max Scaling term is another name for normalizing your data. Normalization doesn’t handle outliers as well as standardization.
Option D is correct. Standardization centers your feature values around the mean. So, it has no bounding range. Therefore, standardization handles outliers much better than normalization.
References:
Please see the article titled Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization (https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/),
The Amazon Machine Learning developer guide titled Data Transformations Reference (https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html)