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Mental Models
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...
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...
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....
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...
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...
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...
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...
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...
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 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...
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