AI-900: Classification: A Supervised Machine Learning Technique

Learn what classification is and how it works as a supervised machine learning technique. Find out how to train and use a classification model for the AI-900 exam.

Question

Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label. True or False?

A. True
B. False

Answer

A. True

Explanation

Classification is an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.

The correct answer is A. True. Classification is indeed an example of a supervised machine learning technique in which you train a model using data that includes both the features and known values for the label, so that the model learns to fit the feature combinations to the label.

Supervised machine learning is a type of machine learning where you provide the model with labeled training data, which means that each example in the data consists of one or more features (input variables) and a label (output variable) that indicates the desired outcome. The model then learns from the training data and applies its learning to new data that it has not seen before, making predictions based on the features of the data.

Classification is a common supervised machine learning task where the label is a discrete category, such as “spam” or “not spam” for email messages, or “dog” or “cat” for animal images. The model learns to associate the features of the data with the label, and then predicts the label for new data based on the features. For example, if you train a classification model using images of dogs and cats, and provide the label for each image, the model will learn to recognize the features that distinguish dogs from cats, such as the shape of the ears, the length of the tail, the color of the fur, etc. Then, if you give the model a new image of a dog or a cat that it has not seen before, it will be able to predict the label based on the features of the image.

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.