Simply now
“Lazy Studying” is machine studying (https://medium.com/@boutnaru/introduction-to-machine-learning-9b488f02efb9) method. In case of lazy studying the coaching knowledge will not be analyzed till the mannequin must make a prediction. Thus, algorithms based mostly on lazy studying “memorize” the coaching knowledge relatively than assemble a basic mannequin (https://www.datacamp.com/blog/what-is-lazy-learning).
Total, we will use lazy studying with classification fashions (https://medium.com/@boutnaru/the-artificial-intelligence-journey-classification-ea539e713fd2). When we now have a brand new object we wish to classify the mannequin and search the coaching knowledge for essentially the most comparable objects. By the way in which, lazy learners are based mostly on similarity metrics for locating essentially the most comparable examples to the enter given (https://www.baeldung.com/cs/lazy-vs-eager-learning) — as proven under.
Lastly, among the many machine studying algorithms that are based mostly on lazy studying we will discover: kNN (k-nearest neighbors), RBF (Radial Foundation Operate), LVQ (Studying Vector Quantization) and CBR (Case-Based mostly Reasoning) — extra on these and different in future writeups (https://www.sciencedirect.com/topics/computer-science/k-nearest-neighbor-classification).
See you in my subsequent writeup 😉 You’ll be able to comply with me on twitter — @boutnaru (https://twitter.com/boutnaru). Additionally, you may learn my different writeups on medium — https://medium.com/@boutnaru. You will discover my free eBooks at https://TheLearningJourneyEbooks.com.
