The ML Algorithm Selector: When to Use Which Machine Learning Algorithm
Author(s): Rohan Mistry
Originally published on Towards AI.
You Know How Every Algorithm Works. But You Have No Idea Which One to Actually Use.
You aced your ML course. You know Random Forest, XGBoost, SVM, Neural Networks.
This article addresses the confusion many face when selecting the appropriate machine learning algorithm for different projects. It emphasizes that while many ML courses teach how algorithms function, they often neglect to instruct students on the practical considerations needed to choose the right one for specific problems. By providing a systematic method for algorithm selection based on problem type, data size, and interpretability requirements, the author aims to empower data scientists to make confident, logic-based decisions rather than relying on guesswork.
Read the full blog for free on Medium.
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Note: Article content contains the views of the contributing authors and not Towards AI.
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