You work as a machine learning specialist for a company that produces polling data and uses it for predictive modeling. Your company wants to build an election prediction model that uses multiple independent variables such as age of the voter, religion, sex, registered affiliation, etc. to predict the candidate for which each observed voter will vote in the upcoming election.
Which type of algorithm is NOT a good choice to use for your prediction? (Select FOUR)

Answer options:

A.Ordinary Least Squares Regression (OLSR)
B.Local Outlier Factor (LOF)
C.Naive Bayes
D.Least-Angle Regression (LARS)
E.K-Means

Answers: A, B, D, E
Option A is correct. Ordinary Least Squares Regression (OLSR) is a regression technique that predicts a dependent variable using one or more independent variables. You are trying to solve a classification problem, which candidate, from a discrete list of candidates, will a voter choose.
Option B is correct. The Local Outlier Factor (LOF) algorithm is used to discover outlier data points. So this would NOT be a good choice for your algorithm where you are trying to solve a classification problem, which candidate, from a discrete list of candidates, will a voter choose.
 Option C is incorrect. The Naive Bayes algorithm can be used as a classifier. You are trying to solve a classification problem, which candidate, from a discrete list of candidates, will a voter choose.
Option D is correct. Least-Angle Regression (LARS) is also a regression technique that predicts a dependent variable using one or more independent variables. You are trying to solve a classification problem, which candidate, from a discrete list of candidates, will a voter choose.
Option E is correct. The K-Means algorithm is used as a clustering algorithm, so it would NOT be a good choice for your algorithm where you are trying to solve for a dependent variable based on multiple independent variables.
Reference:
Please see the Amazon Machine Learning developer guide titled Regression Model Insights, and the article titled A Tour of the Most Popular Machine Learning Algorithms.