Of the following examples, which would you address using an "unsupervised" learning algorithm?

Question

BlackTom AI · Accepted Answer

Question restatement: Which of the following examples would you address using an 'unsupervised' learning algorithm?

Option A: Given emails labelled as spam/not spam, learn a spam filter
Analysis: This is a classic supervised learning scenario because the data comes with labels (spam or not spam) that guide the model to learn a mapping from features to a label. The algorithm learns to predict the label for new emails based on those labeled examples.

Option B: Given a set of news articles found on the web, group them into set of articles about the same story
Analysis: This task involves grouping or clustering articles based on content similarity without predefined story labels. There are no ground-truth labels provided for each article in the setup, which aligns with unsupervised learning, where the goal is to discover structure or patterns in the data.

Option C: Given a database of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not
Analysis: This is another supervised learning scenario, since the dataset includes labels indicating whether each patient has diabetes. The model would learn a decision function to assign a diabetes status to new patients based on labeled examples.

Summary of reasoning across options: Option A relies on labeled outcomes to train a classifier, which is characteristic of supervised learning. Option C also uses labeled outcomes for classification, another hallmark of supervised learning. In contrast, Option B lacks explicit labels for the stories and aims to group similar items, which is the core objective of unsupervised learning through clustering or topic modeling techniques. Misconceptions to avoid: thinking that any data with some form of target could be supervised is common; the key is explicit target labels guiding prediction. Conversely, clustering-based tasks intentionally do not use target labels, and the algorithm infers structure from the data itself.

Overall, the unsupervised learning example is the one that seeks to discover groupings among articles without provided story labels, differentiating it from the labeled classification tasks in Options A and C.

Of the following examples, which would you address using an "unsupervised" learning algorithm?

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