Table of Contents
Which machine learning type predicts binary outcomes like course completion?
Master the AI-900 exam by understanding which machine learning type predicts binary outcomes like whether a student will complete a university course. Learn why classification is the right answer and how it differs from regression, clustering, and translating for prediction tasks.
Question
Which type of machine learning matches the following use case? “Predict whether a student will complete a University course.”
A. Translating
B. Classification
C. Regression
D. Clustering
Answer
B. Classification
Explanation
The correct type of machine learning for predicting whether a student will complete a university course is B. Classification. This scenario involves predicting a discrete categorical outcome with two possible values: complete or not complete.
Understanding Classification
Classification is a supervised machine learning technique used to predict discrete categories or classes. In this case, the model would be trained on historical data containing student features (such as age, previous academic performance, study hours, attendance rates, and socioeconomic factors) along with their known outcomes (whether they completed the course or not). The algorithm learns patterns in the data to distinguish between students who are likely to complete the course versus those who are not. The output is a categorical prediction, typically expressed as a probability of belonging to each class.
Binary vs. Multi-class Classification
This specific scenario represents binary classification because there are only two possible outcomes: the student either completes the course or does not. Binary classification is one of the most common types of classification problems in machine learning and is frequently tested on the AI-900 exam.
Why Other Options Are Incorrect
- Translating: This is not a machine learning type but rather a natural language processing task that involves converting text from one language to another. It has no relevance to predicting student outcomes.
- Regression: This technique predicts continuous numerical values, such as a student’s final grade percentage or GPA. Since the question asks for a yes/no outcome rather than a numerical score, regression is not appropriate.
- Clustering: This is an unsupervised learning technique used to group similar data points together without predefined categories. It would identify groups of students with similar characteristics but would not predict specific outcomes for individual students.
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