supervised-unsupervised-learning

AI Essentials - Grok · 17 questions

• What is the primary goal of a clustering method such as k-means?
• What is the fundamental distinction between a supervised learning problem and an unsupervised learning problem?
• Which of the following tasks is an example of unsupervised learning?
• A retail analytics team is using K-means to group shoppers for a loyalty campaign. Which statement best captures a primary limitation of this algorithm?
• In terms of how training data is used, what primarily distinguishes semi-supervised learning from standard supervised learning?
• What is the primary goal of clustering algorithms in machine learning?
• A retail analytics group wants to split shoppers into natural segments for personalized campaigns, but no segment labels exist in the data. Which combination of learning paradigm and algorithm is most appropriate?
• What is the primary distinction between linear regression and logistic regression?
• A subscription business wants a model that predicts whether each customer will cancel service in the next 30 days. What type of machine learning problem is this?
• A trading analytics team needs a model to forecast the next-day closing price of equities as a numeric value. What type of machine learning problem is this?
• While fitting a linear regression model to estimate home sale prices from property attributes, which assumption about how predictors relate to the target is fundamental to the model form?
• A team trains a model to estimate each customer’s expected monetary lifetime value as a continuous dollar amount. Which type of machine learning problem is this?
• What is the principal distinction between a single decision tree classifier and a random forest classifier?
• When is unsupervised learning typically the most appropriate modeling approach?
• You are scoping a new AI project and must classify the learning paradigm before choosing algorithms. Which condition most clearly indicates that the problem is a supervised learning task?
• In supervised learning problem framing, how do regression and classification primarily differ?
• Marketing wants natural groupings of customers for a campaign, but the company has no pre-labeled "segment" field—only behavioral and demographic attributes. Which problem formulation and primary practical challenge best match this goal?