ITGSS Certified Technical Associate: Project Management Practice Exam

What indicates that a Regression Machine Learning model is supervised?

1. It requires unsupervised data for training

2. It uses labeled input data for predictions

3. It generates random predictions

4. It analyzes data without prior knowledge

The correct answer is: It uses labeled input data for predictions

A Regression Machine Learning model is considered supervised because it relies on labeled input data during the training process. In supervised learning, the model learns to make predictions based on the relationship between the input data (features) and the corresponding known output data (labels or targets). This training allows the model to generalize and make reliable predictions on unseen data. When presented with labeled data, the regression model can identify patterns and relationships that exist in the data set, which enables it to predict continuous outcomes accurately. For instance, in a typical regression task, a dataset might consist of features such as square footage, number of bedrooms, and location, with the label being the sale price of a house. The model trains on this pre-labeled data to establish connections between the features and the target, thus enhancing its predictive capability. In contrast, the other options mention concepts related to unsupervised learning or provide incorrect descriptions of the model's functionality. This distinction is crucial in understanding the foundational elements of machine learning, specifically the differences between supervised and unsupervised approaches.