Learn about the most likely scenarios where logistic regression would be used, including predicting customer purchasing likelihood based on browsing history. Prepare for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
In which scenario would logistic regression most likely be used?
A. Predicting the grades of students based on the number of extracurricular activities they participate in.
B. Analyzing the correlation between income and education level.
C. Determining the relationship between temperature and ice cream sales.
D. Predicting the likelihood of customers purchasing a product based on their browsing history.
Answer
The scenario where logistic regression would most likely be used is:
D. Predicting the likelihood of customers purchasing a product based on their browsing history.
Explanation
Logistic regression would be suitable for predicting the likelihood of a binary outcome (purchase or no purchase) based on browsing history, which is categorical.
Logistic regression is a supervised machine learning algorithm used for binary classification problems, where the goal is to predict a categorical outcome (usually 0 or 1, yes or no) based on one or more predictor variables.
In the given scenario, predicting whether a customer will purchase a product (a binary outcome) based on their browsing history (the predictor variables) is a textbook example of where logistic regression excels. The algorithm can learn patterns in the browsing data that correlate with higher or lower purchasing probability, and output a likelihood between 0 and 1 that the customer will make a purchase.
The other options involve tasks better suited for different algorithms:
A) Predicting student grades (a continuous outcome) based on number of extracurriculars would use linear regression.
B) Analyzing correlations is done with statistical methods like Pearson or Spearman correlation.
C) Determining relationships between two continuous variables like temperature and sales is a job for linear regression.
But for estimating the probability of a binary event based on known predictors, logistic regression is the go-to algorithm. Its ability to output well-calibrated probabilities makes it ideal for applications like predicting customer actions based on behavioral data.
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