Learn how to select the best machine learning algorithm for a concert recommendation service based on user preferences, artist information, and event location. Discover the key differences between decision trees, logistic regression, supervised learning, and reinforcement learning in the context of the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
You’ve been asked to create a machine learning service that helps people choose what concert to attend on a particular date based on the type of music they prefer, who is singing, and where the event is taking place.
What type of algorithm should you use?
A. Decision tree
B. Logistic regression
C. Supervised learning
D. Reinforcement learning
Answer
A. Decision tree
Explanation
You’d use a decision tree to decide based on factors such as date, distance, performer, and so on, which can be used to flow through the branches of choices to arrive at viable options.
For creating a machine learning service that helps people choose concerts based on music preferences, artists, and event locations, the most suitable type of algorithm is decision tree
Decision trees are commonly used for classification tasks, and they can help make decisions based on multiple features (such as music genre, artist, and location). By constructing a tree-like structure, decision trees can guide users toward concert choices based on their preferences. Each branch of the tree represents a decision based on specific features, leading to a final recommendation.
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