Learn why multinomial logistic regression is the ideal machine learning model for predicting multiple outcomes, such as roulette table colors (red, black, or green).
Table of Contents
Question
You want to create a machine learning model that provides a prediction for three possible colors of a roulette table that you can place a bet on: red, black, or green. Which of the following machine learning models should you use?
A. Linear regression
B. Binary logistic regression
C. Polynomial regression
D. Multinomial logistic regression
Answer
D. Multinomial logistic regression
Explanation
You would use a multinomial logistic regression machine learning model. This system is specifically designed for multi-class classification problems. It extends the concept of binary logistic regression such as true/false outcomes to handle scenarios with more than two possible outcomes, such as intransitive games of rock-paper-scissors, weather states of rain, cold, or windy, and colors on a roulette table. It uses a one-vs-rest strategy, training multiple logistic regression models, each differentiating one class from the others. This allows the model to learn the unique characteristics of each color on the roulette table and predict the most likely category for new data points.
You would not use polynomial regression. This technique focuses on fitting a non-linear relationship between features and a continuous numerical value. It utilizes polynomial functions of varying degrees to capture complex, curved relationships between features and the target variable. It can be used for predicting stock prices, sales figures, population growth, or other scenarios where the relationship between variables is not linear.
You would not use binary logistic regression. This algorithm is designed for classification problems where there are only two possible outcomes. In this case, you have three colors, requiring a model capable of handling multiple categories.
You would not use linear regression. Linear regression utilizes a linear equation to estimate the best-fitting straight line that represents the relationship between features and the target variable. It can be used for predicting a customer lifetime value, loan defaults, or other scenarios where the relationship between variables is assumed to be linear.
Microsoft Azure AI Fundamentals AI-900 certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Microsoft Azure AI Fundamentals AI-900 exam and earn Microsoft Azure AI Fundamentals AI-900 certification.