You work for a car manufacturer as a machine learning specialist. Your marketing team wants to use a marketing strategy to market to different consumer segments based on how the features of each of their cars resonate with their customer base.
The dataset with which you have to work contains many features about each car, such as color, size, number of doors, number of speakers, type of roof, type of auto-assist, etc. Through your exploratory modeling, you have found many of these features are redundant, meaning they don’t offer anything further to your algorithm’s performance.
Your dataset contains a large number of observations and a large number of features. How would you solve this redundant feature problem most efficiently and expeditiously?

Answer options:

A.Keep all the features and use the XGBoost algorithm to account for redundant features.
B.Use Sparse Feature Graph to remove the redundant features.
C.Use Principal Component Analysis to reduce the number of features.
D.Keep all the features and use the Random Cut Forest algorithm to account for redundant features.

Answer: C
Option A is incorrect. The XGBoost algorithm is used to predict a target variable in a very fast and efficient manner. However, the XGBoost will not automatically adjust for redundant features. The redundant features will act as a performance drag since you have a large number of features and a large number of observations.
Option B is incorrect. Removing the redundant features outright creates the risk of information loss. A better solution is to find composites of uncorrelated features, which is the technique used by Principal Component Analysis.
Option C is correct. Principal Component Analysis is a machine learning algorithm that reduces dimensionality within your data without sacrificing information. It does this by finding composites of features that are uncorrelated.
Option D is incorrect. The Random Cut Forest algorithm is used to find atypical data points in a dataset. Therefore it will not help find redundant features. The redundant features will act as a performance drag since you have a large number of features and a large number of observations.
Reference:
Please see the Amazon SageMaker developer guide titled Using Amazon SageMaker Built-in Algorithms, the Amazon SageMaker developer guide titled Principal Component Analysis (PCA) Algorithm, and the article titled Automatically Redundant Features Removal for Unsupervised Feature Selection via Sparse Feature Graph.