You work as a machine learning specialist for an online gambling software company. Your online app allows users to gamble on the outcomes of sporting matches, such as football, basketball, cricket, etc. Your machine learning team is responsible for predicting the score difference outcomes of these matches so your company can set the betting line. For example, team A will beat team B by 7.5 wickets, where 7.5 is the betting line. Your data sources for your models contain many features, such as team power ranking, previous match score differences, player injury reports, etc. You have transformed your data to make all features numeric (either counts or continuous values). However, through your data discovery you have noticed that some of your features are multicollinear. How can you address the multicollinearity of your features?

Answer options:

A.Use Linear Discriminant Analysis (LDA) to reduce your model’s dimensionality, then drop the resulting components that have high variance. 
B.Use Linear Discriminant Analysis (LDA) to reduce your model’s dimensionality, then drop the resulting components that have very low variance.
C.Use Principal Component Analysis (PCA) to reduce your model’s dimensionality, then drop the resulting components that have very low variance.
D.Use Principal Component Analysis (PCA) to reduce your model’s dimensionality, then drop the resulting components that have high variance.

Correct Answer: C
Option A is incorrect. Linear Discriminant Analysis (LDA) is used to reduce dimensionality in multi-class classification problems that predict a categorical target. We are trying to solve for a continuous target, match point difference, or spread. Also, you want to solve for very low variance when you use a more appropriate dimensionality reduction algorithm.
Option B is incorrect. Linear Discriminant Analysis (LDA) is used to reduce dimensionality in multi-class classification problems that predict a categorical target. We are trying to solve for a continuous target, match point difference, or spread.
Option C is correct. Using Principal Component Analysis (PCA) to reduce the dimensionality of your feature set by dropping the components that have very low variance will remove the multicollinearity of your features.
Option D is incorrect. Principal Component Analysis (PCA) is the correct choice of algorithm to remove the multicollinearity of your features. However, you want to drop the components that have very low variance, not the components that have high variance.
Reference:
Please see the Towards Data Science article titled Multicollinearity — How does it create a problem? (https://towardsdatascience.com/https-towardsdatascience-com-multicollinearity-how-does-it-create-a-problem-72956a49058), and the Kaggle page titled Principal Component Analysis (https://www.kaggle.com/ryanholbrook/principal-component-analysis), the Towards Data Science article titled A beginner’s guide to dimensionality reduction in Machine Learning (https://towardsdatascience.com/dimensionality-reduction-for-machine-learning-80a46c2ebb7e), the Machine Learning Mastery article titled Linear Discriminant Analysis for Machine Learning (https://machinelearningmastery.com/linear-discriminant-analysis-for-machine-learning/), the StatsTest.com article titled Linear Discriminant Analysis (https://www.statstest.com/linear-discriminant-analysis/), and the Machine Learning Mastery article titled Linear Discriminant Analysis for Dimensionality Reduction in Python (https://machinelearningmastery.com/linear-discriminant-analysis-for-dimensionality-reduction-in-python/)