Discover how regression models in machine learning are used to predict the numeric value of house prices. Learn about the differences between regression, reinforcement learning, and classification models.
Table of Contents
Question
A machine learning model that predicts the numeric value that a house will sell for.
Which type of problem solving model is this use case?
A. Regression
B. Reinforcement learning
C. Multiclass classification
D. Binary classification
Answer
A. Regression
Explanation
The given use case, predicting the numeric value that a house will sell for, is an example of a regression problem in machine learning. Regression models are used when the target variable (the value to be predicted) is a continuous numeric value, such as house prices, stock prices, or temperatures.
In this scenario, the machine learning model would be trained on a dataset containing various features of houses (e.g., square footage, number of bedrooms, location) along with their corresponding sale prices. The model learns the relationships between these features and the target variable (sale price) to make predictions on new, unseen data points.
Reinforcement learning (B) is not applicable in this case, as it involves an agent learning to make decisions in an environment to maximize a reward signal. It is more suitable for problems like game playing or robot navigation.
Multiclass classification (C) and binary classification (D) are also not applicable, as they involve predicting discrete categories or classes. Multiclass classification is used when there are more than two possible output classes, while binary classification is used when there are only two possible output classes (e.g., spam vs. not spam).
In summary, predicting the numeric value that a house will sell for is a regression problem, as it involves predicting a continuous numeric value based on input features.
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