You are a machine learning specialist at an online car retailer. Your machine learning team has been tasked with building models to predict car sales and customer conversion rates. The dataset you are using has a large number of features, over 1,000. Your team plans to use linear models, such as linear regression and logistic regression, in a SageMaker Studio environment. When your team performs exploratory data analysis in their SageMaker Studio jupyter notebooks, they notice that many features are highly correlated with each other. Your tech lead has indicated that this may make your models unstable.
Which option would help you reduce the impact of having such a large number of features?

Answer options:

A.Use dot product on the highly correlated features.
B.Use Principal Component Analysis (PCA) to create a new feature space
C.One-hot-encode the highly correlated features.
D.Use TF-IDF encoding to reduce the impact of the highly correlated features.

Answer: B
Option A is incorrect. Dot product or matrix multiplication will not reduce the impact of having 1,000 features in your dataset. It is used in deep learning for operations such as the Softmax function.
Option B is CORRECT. Principal Component Analysis (PCA) is a very common technique used in machine learning to reduce the dimensionality of your dataset. Reducing the dimensionality reduces the impact of having a large number of correlated features.
Option C is incorrect. One-hot-encoding is a technique used to encode categorical data. One-hot-encoding will actually increase the number of features in your dataset.
Option D is incorrect. TF-IDF, or Term Frequency Inverse Document Frequency, is used to indicate the importance of a word in a document in a collection or corpus. You are dealing with sales data and conversion rates, not text datasets.
Reference:
Please see the Amazon SageMaker developer guide titled Amazon SageMaker Studio.
Please refer to the Data Science Bootcamp article titled Understand Dot Products Matrix Multiplications Usage in Deep Learning in Minutes — beginner friendly tutorial.
Please see the Data Science Bootcamp article titled Understand the Softmax Function in Minutes.
Please see the article titled A simple guide to One-hot Encoding, tf and tf-idf Representation.