Learn which type of machine learning algorithm – clustering, regression or classification – is best suited for predicting the animal population size in a given area. Get a clear, expert-level explanation to ace your AI-900 Microsoft Azure AI Fundamentals certification exam.
Table of Contents
Question
In the given scenario, select which type of machine learning you should use. Predict the animal population of an area.
A. clustering.
B. regression.
C. classification.
Answer
B. regression.
Explanation
Regression is a type of supervised machine learning used to predict a continuous numerical value, such as population size, based on input features/variables. The goal is to find a mathematical relationship between the input variables and output variable to make predictions.
Regression algorithms like linear regression, decision trees, or neural networks learn a function that maps the input features to the target numeric value. After training on historical data with known input-output pairs, the model can then take in new input data and estimate the corresponding output value.
In this scenario, a regression model could be trained on factors like geographic area, habitat type, food availability, etc. as inputs to predict the animal population size as the output. The model learns the relationships between these variables and population, allowing it to estimate population for new areas.
In contrast:
- Classification predicts discrete class labels or categories, not continuous values
- Clustering finds inherent structure and groupings in data without predicting a target variable
Therefore, regression is the appropriate machine learning approach for predicting animal population size in this scenario.
Regression is used to predict continuous numerical values, such as the animal population in a specific area. It identifies relationships between variables and provides an estimate of a quantity, making it suitable for this scenario.
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