What is Feature Engineering in Machine Learning?
Feature engineering sounds more technical than it is. In machine learning, it means turning raw data into useful inputs for a model. If you have ever created a calculated column in a spreadsheet, grouped records into categories in SQL, or turned a date into “days since last purchase,” you have already done a version of […]
What Is Cross Validation in Machine Learning
Cross validation in machine learning is not just a technique for testing a model. It is a way of asking a harder question than a single test can answer. A model tested once on one slice of data might look accurate because that slice happened to suit it. Cross validation tests the model repeatedly on […]
What Is Overfitting in Machine Learning
Overfitting is not a model failing to learn. It is a model that learned exactly what it was shown, and nothing more. A model with overfitting in machine learning has adjusted its parameters so precisely to the training data that it picks up patterns specific to that dataset rather than patterns that hold more broadly. […]
What Regularization Does (and Why Your Model Needs It)
Overfitting is not a mistake the model makes. It is what happens when a model does exactly what it is told. Regularization in machine learning is not just a way to prevent overfitting. It changes what the model is allowed to learn in the first place. Instead of only minimizing error on the training data, […]
Machine Learning for Non-Technical Professionals
Why Your “Silly Questions” Are Product Requirements We have a technical writer on our team. She’s sharp, curious, and exactly the kind of non-technical professional who needs to understand machine learning to do her job well. Yesterday after a team meeting, she sent me a Slack message. My boss had explained that we needed to […]
A Friendly Introduction to Principal Component Analysis
Most datasets don’t have two or three measurements per observation. They have dozens, sometimes hundreds. A patient record might include blood pressure, cholesterol, glucose levels, BMI, age, and dozens of lab results. A manufacturing sensor array might log temperature, vibration, pressure, humidity, and electrical output from every machine on the floor, every few seconds. The […]
Bias Variance Tradeoff: Why Your ML Model Can’t Have It All
For years, every time I fixed one problem with my models, I created another one. Make the model more sophisticated to capture complex patterns? It performs terribly on new data. Simplify it to work better on new examples? Now it’s missing obvious relationships in the training data. If that sounds familiar, you’re experiencing the bias […]
Training vs Testing Data: ML Models Must Prove Themselves
Does this sound familiar? You’re tutoring a student for an upcoming math test. You help them solve dozens of practice problems over several days, and by the end, they’re getting every problem right. You feel confident they’ve mastered the material. The same problem shows up when splitting training and testing data in machine learning, and […]
Inference vs Prediction in Machine Learning: What’s the Difference?
Some machine learning applications can make accurate recommendations but can’t explain the reasoning. Others provide clear explanations but aren’t quite as precise. That tradeoff between inference and prediction in machine learning shows up early in any project: do you need the most accurate answer possible, or do you need to understand why that answer makes […]
Classification vs Regression: Predicting What vs. How Much
In our previous post, we explored supervised vs. unsupervised learning. Now we’re diving into another fundamental choice you’ll face in every supervised learning project: are you trying to predict what category something belongs to, or are you trying to predict a specific number? This might sound like a subtle distinction, but it completely changes how […]
Supervised vs Unsupervised Learning in Machine Learning: What the Names Don’t Tell You
Remember learning to ride a bike? Some of us had a parent running alongside, holding the seat and shouting guidance: “Pedal faster! Look ahead! Balance!” Others figured it out through trial and error, falling, getting back up, and gradually finding that magic balance point with no one giving instructions. These two approaches perfectly capture the […]