…and how to increase your chances of avoiding it — If you use a tool where it hasn’t been verified safe, any mess you make is your fault… AI is a tool like any other, so the same rule applies. Never trust it blindly. Instead, force machine learning and AI systems to earn your trust. If you want to teach…

Avoiding the classic data science newbie mistake — A great way to keep your wits about you when working with machine learning (ML) and artificial intelligence (AI) is to think like a teacher. After all, the point of ML/AI is that you’re getting your (machine) student to learn a task by giving examples instead of explicit instructions. As…

On data science’s self-appointed language police — When the science of psychology was still in its infancy, I imagine there might have been quite a few academic correspondences of this flavor: Psychologist 1: “Dear sir! That which you are calling ‘memory’ is not, in fact, memory! Instead the following is memory…” Psychologist 2: “Dear sir! I quite…

The role of causal inference and statistical reasoning in science — Every good scientific experiment that seeks to establish how something works — and not just that it does work — is about making clones. Huh? Bear with me. Thinking scientifically Let’s arm wrestle! To prepare, you drink a dram of Strength Potion (TM) while I drink filtered water. Then…

Mean squared error (MSE) for performance evaluation and optimization — Let’s talk about a crucial piece that’s subtly missing from most discussions about the mean squared error (MSE): metrics and loss functions are not quite the same thing. You need two separate scoring functions for model performance evaluation and optimization in machine learning. …

4 reasons to wait until your team is more mature — Many organizations start their data science journeys by overemphasizing AI research and gorging themselves on PhDs in their hiring frenzy. That’s a good idea if: You’re investing in a research division and you’re not planning to ask your researchers what they’ve done for you lately. You already know you’re solving…

The journey to becoming a “real” data analyst — Analytics is like the writing profession: the basics are easy to get started with and it’s an art, so there are few barriers to entry and anyone can call themselves a “writer.” There’s no guarantee of quality in the title. However, being literate enough to write some tweets hardly makes…

…and why does your machine learning project need both? — Have you been using your loss function for evaluating your machine learning system’s performance? That's a mistake, but don’t worry, you’re not alone. It’s a widespread misunderstanding that may have something to do with software defaults, college course format, and decision-maker absenteeism in AI. In this article, I’ll explain why…

Introducing residuals and the mean squared error (MSE) — The mean squared error (MSE) is one of many metrics you could use to measure your model’s performance. If you take a machine learning class, chances are you’ll come across it very early in the syllabus — it’s usually baby’s first loss function for continuous data. That’s a lot of…