AI-900: Models that Predict Between Categories

Learn how to answer a common AI-900 exam question about models that predict between categories, such as multi-class neural network and two-class logistic regression.

Question

Which of the following are models that help predict between two or several categories?
Select all that apply.

A. Multi-class neural network
B. Linear Regression
C. Two-class logistic regression
D. Two-class decision forest

Answer

A. Multi-class neural network
C. Two-class logistic regression

Explanation

Two-class decision forests and Two-class logistic regressions help predict between two categories, while Multi-class neural networks help predict between several categories.

The correct answer is A and C. A multi-class neural network is a model that can predict between more than two categories, such as classifying images of animals into dogs, cats, or birds. A two-class logistic regression is a model that can predict between two categories, such as classifying emails as spam or not spam. Both of these models are examples of classification models, which are used to assign labels to data based on some features.

B and D are incorrect because they are not models that help predict between categories, but rather models that help predict numerical values or probabilities. Linear regression is a model that can predict a continuous numerical value, such as the price of a house based on its size and location. Two-class decision forest is a model that can predict the probability of belonging to one of two categories, such as the likelihood of a customer buying a product based on their age and gender. Both of these models are examples of regression models, which are used to estimate the relationship between variables.

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.