Generative AI Certificate Q&A: What machine learning algorithm to look at each predictor independently and come up with overall score?

Question

You work for a large financial institution that would like to offer immediate approval for loan applications. Your team has identified four predictors about whether someone will be a good loan candidate: income, credit score, employment, and debt. You develop a system that will look at each predictor independently and then come up with an overall score. What machine learning algorithm are you using?

A. Naive Bayes
B. K-means clustering
C. K-nearest neighbor
D. Linear regression

Answer

D. Linear regression

Explanation

The correct answer to the question is D. Linear regression. Here’s a detailed explanation to elaborate on this answer:

In the described scenario, the team has identified four predictors (income, credit score, employment, and debt) to determine whether someone will be a good loan candidate. The team has developed a system that assesses each predictor independently and derives an overall score. The machine learning algorithm used in this case is:

D. Linear regression: Linear regression is a supervised learning algorithm used for predicting a continuous numerical value based on one or more input features. In this scenario, the system is assigning an overall score to each loan application based on the predictors.

The independent predictors, such as income, credit score, employment, and debt, act as the input features or variables for the linear regression model. The model aims to learn the relationship between these predictors and the overall loan candidate score.

Linear regression assumes a linear relationship between the input features and the target variable (in this case, the overall score). By training the linear regression model on a labeled dataset that includes historical loan data, the model learns the coefficients or weights associated with each predictor to best fit the data and make accurate predictions.

Once trained, the linear regression model can take new loan applications, evaluate the values of the predictors (income, credit score, employment, and debt), and generate a predicted overall score based on the learned relationship between the predictors and the target variable.

Options A, B, and C are incorrect because they do not align with the described scenario:

• A. Naive Bayes: Naive Bayes is a probabilistic classification algorithm used for predicting discrete categorical outcomes. It assumes independence among the input features, which may not be suitable for predicting an overall score based on multiple continuous predictors.
• B. K-means clustering: K-means clustering is an unsupervised learning algorithm used for grouping or clustering data points based on similarity. It is not appropriate for the scenario described since the goal is to assign an overall score to each loan application rather than grouping them based on similarity.
• C. K-nearest neighbor: K-nearest neighbor (KNN) is a supervised learning algorithm used for classification or regression tasks. However, it does not directly align with the described scenario, where an overall score is derived based on the predictors. KNN relies on finding the k-nearest neighbors in the feature space, which may not be the most appropriate approach for this specific case.

In summary, the system developed by the team in the described scenario, which assesses loan applicants based on four predictors and assigns an overall score, utilizes the linear regression algorithm. Linear regression allows for the prediction of a continuous value, such as the overall score, based on the relationship between the independent predictors and the target variable.

Reference

• 10 Machine Learning Algorithms to Know in 2023 | Coursera
• A Tour of Machine Learning Algorithms (machinelearningmastery.com)
• Machine Learning Algorithms | Know Top 8 Machine Learning Algorithms (educba.com)
• Machine Learning Algorithms | Microsoft Azure
• Robot With Education Hud High-Res Stock Photo – Getty Images
• Machine Learning in Banking: How Banks Approve Automated Loans (neurochaintech.io)
• Loan Approval Prediction using Machine Learning – GeeksforGeeks
• Credit Risk Modeling Using Machine Learning Algorithm | by Sarit Maitra | The Startup | Medium
• Machine Learning Models for Predicting Bank Loan Eligibility | IEEE Conference Publication | IEEE Xplore
• An Approach for Prediction of Loan Approval using Machine Learning Algorithm | IEEE Conference Publication | IEEE Xplore

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