Which Algorithm to Avoid for Your Election Prediction Model?
Question
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)
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D. E.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.
Out of the given options, the following four algorithms are NOT suitable for building an election prediction model using multiple independent variables:
B. Local Outlier Factor (LOF) D. Least-Angle Regression (LARS) E. K-Means. A. Ordinary Least Squares Regression (OLSR)
Here's why:
B. Local Outlier Factor (LOF): The Local Outlier Factor algorithm is an unsupervised anomaly detection algorithm that identifies anomalies in data points that deviate from the surrounding data points' density. While it can be useful for detecting outliers, it is not suitable for predicting the outcome of an election based on multiple independent variables.
D. Least-Angle Regression (LARS): Least-Angle Regression is a regression analysis algorithm that finds the best linear model that explains the relationship between the predictor variables and the outcome variable. However, it is not suitable for predicting election outcomes based on multiple independent variables.
E. K-Means: K-Means is a clustering algorithm that groups data points into K clusters based on their similarity to each other. It is not suitable for building a predictive model for election outcomes based on multiple independent variables.
A. Ordinary Least Squares Regression (OLSR): Ordinary Least Squares Regression is a linear regression analysis algorithm that finds the best-fit line for the relationship between the predictor variables and the outcome variable. While it can be used for prediction, it may not be the best choice for election outcome prediction based on multiple independent variables.
In contrast, Naive Bayes is a classification algorithm that can be used for predicting the categorical outcome of an event based on multiple independent variables. It is a popular choice for text classification and spam filtering and can be suitable for building an election prediction model as well.
To summarize, while there are many algorithms that can be used to build a predictive model for election outcomes, the given options suggest that unsupervised algorithms and algorithms not optimized for multiple independent variables are not suitable for this task.
