How to Design Machine Learning Experiments — the Right Way | Towards Data Science

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It’s tempting to think that what separates a successful machine learning project from a not-so-great one is a cutting-edge model, more computing power, or a few extra teammates.

In reality, throwing more resources at a poorly conceived problem rarely works—and in the rare instance where it does, you end up being stuck with an inefficient solution. 

The articles we’re highlighting this week demonstrate, each in its own way, how important it is to ask the right questions, and to design experiments that stand a good chance to answer them (or to teach you valuable lessons when they don’t). Let’s dive in.

How Do Grayscale Images Affect Visual Anomaly Detection?

Focused, concise, and pragmatic, Aimira Baitieva‘s walkthrough tackles a common computer vision problem, and offers insights on experiment design that you can apply across a wide range of projects where speed and performance are crucial.

A Well-Designed Experiment Can Teach You More Than a Time Machine!

Using a “time-machine-based conceptual exercise,” Jarom Hulet sets out to show us the role experimentation can play in uncovering causal relations and making counterfactuals concrete.

When LLMs Try to Reason: Experiments in Text and Vision-Based Abstraction

How far can language and image models go in learning abstract patterns from examples? Alessio Tamburro’s deep dive unpacks findings from a series of thought-provoking tests.

This Week’s Most-Read Stories

Catch up on the articles our community has been buzzing about in recent days:

The ONLY Data Science Roadmap You Need to Get a Job, by Egor Howell

Automated Testing: A Software Engineering Concept Data Scientists Must Know To Succeed, by Benjamin Lee

The Stanford Framework That Turns AI into Your PM Superpower, by Rahul Vir

Other Recommended Reads

From advanced clustering techniques to small-but-mighty vision models, our authors have recently covered both timely and evergreen topics. Here are a few standout reads for you to explore:

• LLMs and Mental Health, by Stephanie Kirmer

• Stellar Flare Detection and Prediction Using Clustering and Machine Learning, by Diksha Sen Chaudhury

• How Not to Mislead with Your Data-Driven Story, by Michal Szudejko

• How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned, by Julio Sanchez

• Getting AI Discovery Right, by Janna Lipenkova

Meet Our New Authors

Explore top-notch work from some of our recently added contributors:

• Juan Carlos Suarez is a data and software engineer whose interests straddle machine learning, medical data analysis, and AI tools.
• Daphne de Klerk shared an article on prompt bias (and how to prevent it), and joins our community with deep product- and project-management expertise.
• Tianyuan Zheng, who recently completed a master’s in computational biology at Cambridge, wrote his debut article on how computers “see” molecules.

We love publishing articles from new authors, so if you’ve recently written an interesting project walkthrough, tutorial, or theoretical reflection on any of our core topics, why not share it with us?

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