Any three of: linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k-nearest neighbors, naive Bayes and neural networks.
Card 1 of 259. Answer: Any three of: linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k-nearest neighbors, naive Bayes and neural networks.
About · Quizzes · History of AI · To suggest new questions or report an error send me a dm.
Logistic regression, decision trees and support vector machines are three; linear regression, k-nearest neighbors, naive Bayes, random forests, gradient boosting and neural networks also count. All learn from labeled examples, inputs paired with the correct output. Regression on its own names a task, predicting a number, not an algorithm.
Take spam filtering, with emails labeled spam or not spam. Logistic regression learns a weight for each word and turns the weighted sum into a probability. A decision tree learns yes or no questions, such as whether the email contains "free". A support vector machine finds the boundary with the widest margin between the classes. Naive Bayes multiplies per-word probabilities, assuming words are independent given the class.
A strong answer spans families and tasks: logistic regression classifies despite its name, linear regression predicts numbers, and trees, KNN, SVMs and neural networks do both. Interviewers then ask how to choose: data size, interpretability, training cost and data type. A classic trap: k-means and PCA use no labels, so they are unsupervised.
