A company plans to develop ML applications to improve business operations and efficiency. For each use case, select the appropriate ML paradigm. Each ML paradigm may be selected one or more times. <Dropdown blanks={[{"id":"binary_classification","options":["Supervised learning","Unsupervised learning"],"answer":"Supervised learning","label":"Binary classification"},{"id":"multiclass_classification","options":["Supervised learning","Unsupervised learning"],"answer":"Supervised learning","label":"Multi-class classification"},{"id":"kmeans_clustering","options":["Supervised learning","Unsupervised learning"],"answer":"Unsupervised learning","label":"K-means clustering"},{"id":"dimensionality_reduction","options":["Supervised learning","Unsupervised learning"],"answer":"Unsupervised learning","label":"Dimensionality reduction"}]} explanation={"Binary and multi-class classification predict labeled class values and therefore use supervised learning. K-means clustering groups unlabeled observations, and dimensionality reduction generally discovers a lower-dimensional representation without labels; both are unsupervised learning.\n\n**Learn more:** [Supervised vs Unsupervised Learning](https://aws.amazon.com/compare/the-difference-between-machine-learning-supervised-and-unsupervised/) · [K-Means Algorithm - Amazon SageMaker AI](https://docs.aws.amazon.com/sagemaker/latest/dg/k-means.html)"} />
Q24Fundamentals of AI and ML
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Binary classification
Supervised learning
(Binary classification involves predicting one of two classes, and it requires labeled data for training.)
Multi-class classification
Supervised learning
(Multi-class classification involves predicting one of multiple classes, and it also requires labeled data for training.)
K-means clustering
Unsupervised learning
(K-means clustering is a technique used to group data into clusters without labeled data, making it an unsupervised learning method.)
Dimensionality reduction
Unsupervised learning
(Dimensionality reduction techniques, such as PCA (Principal Component Analysis), are used to reduce the number of features in a dataset without labeled data, making it unsupervised.)
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Supervised Learning:
• Binary Classification: Requires labeled data (two classes) to train the model.
• Multi-Class Classification: Requires labeled data (more than two classes) to train the model.
Unsupervised Learning:
• K-means Clustering: Does not require labeled data; it identifies natural groupings in the data.
• Dimensionality Reduction: Typically unsupervised; it reduces the number of features based on the inherent structure of the data without using labels.
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Below is the correct answer:
Supervise learning
Supervise learning
Unsupervised learning.
Unsupervised learning
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Binary classification - supervised learning
Multi-class classification - supervised learning
Both techniques involved training models with labeled data
K-means clustering - unsupervised learning
groups data based on similarity but not labels
Dimensionality reduction - unsupervised learning
aim to reduces number of features in dataset and does not need labels