Answer-first summary for fast verification
Answer: K-Nearest Neighbors, Semi-supervised learning
Lazy learning algorithms, such as K-Nearest Neighbors (KNN), are characterized by their approach of delaying the processing of training data until a query is made. This makes KNN an ideal choice for scenarios with uncertain data distributions and limited computational resources, as it does not require a model to be trained upfront. Semi-supervised learning, while not a lazy learning algorithm per se, can be considered in contexts where labeled data is scarce but unlabeled data is abundant, leveraging both for improved model performance. Neural Networks, Logistic Regression, and Naive Bayes, on the other hand, involve upfront model training and thus do not fit the lazy learning paradigm.
Author: LeetQuiz Editorial Team
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In your role as a Data Scientist at a tech startup, you're tasked with developing a predictive model for customer segmentation. Your mentor suggests using a lazy learning algorithm due to its advantages in scenarios where the data distribution is not well understood upfront and computational resources are limited. Given these constraints, which of the following algorithms should you consider implementing? (Choose two options if E is available, otherwise choose one.)
A
Simple Neural Networks
B
K-Nearest Neighbors
C
Logistic Regression
D
Naive Bayes
E
Semi-supervised learning
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