AI-900: How to Use Classification Models for Machine Learning Scenarios

Learn what classification models are and how they can be used to solve various machine learning problems, such as loan approval, churn prediction, and customer segmentation.

Question

Which of the following scenarios can be resolved by applying classification models?

A. A bank wanting to determine if a specific set of clients are eligible for taking a loan.
B. A company who wants to predict the churn rate of their subscribers for next month.
C. A toy company wanting to determine which clients are inclined to buy a specific toy.

Answer

A. A bank wanting to determine if a specific set of clients are eligible for taking a loan.
C. A toy company wanting to determine which clients are inclined to buy a specific toy.

Explanation

Classification is a form of machine learning that is used to predict which category, or class, an item belongs to.

Classification models are a type of supervised machine learning technique that can be used to assign labels or categories to data points based on their features. Classification models can be applied to various scenarios where the goal is to predict a discrete outcome from a set of inputs.

Some examples of classification models are:

• A bank wanting to determine if a specific set of clients are eligible for taking a loan. This is a binary classification problem, where the possible outcomes are yes or no. The bank can use a classification model to learn from the historical data of previous clients, such as their income, credit score, age, etc., and assign a probability of loan approval to each new client based on their features.
• A company who wants to predict the churn rate of their subscribers for next month. This is a multi-class classification problem, where the possible outcomes are more than two. The company can use a classification model to learn from the behavioral data of their subscribers, such as their usage, feedback, payment history, etc., and assign a label of high, medium, or low churn risk to each subscriber based on their features.
• A toy company wanting to determine which clients are inclined to buy a specific toy. This is also a multi-class classification problem, where the possible outcomes are the different types of toys that the company offers. The toy company can use a classification model to learn from the demographic and preference data of their clients, such as their age, gender, location, interests, etc., and assign a label of the most likely toy that each client would buy based on their features.

Therefore, the answer to the question is that all of the scenarios can be resolved by applying classification models.

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