Supervised prediction of numeric outcomes, including regression objectives, models, residual diagnostics, and uncertainty around numeric predictions.
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34 articles in this tag.
Retrain the same model on a different random split and the predictions move. Nobody changed the code. Nobody changed the hyperparameters. The wobble is not…
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You fit ridge, you fit lasso, lasso wins by a hair, and you quietly file away "lasso is better." That conclusion is a coin flip wearing a lab coat. One…
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You fit a linear model. The coefficients look sensible. Each feature seems to contribute its own share, and the numbers align with your intuition. Then you…
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Linear regression draws a straight line through continuous numbers. Logistic regression draws an S-curve that outputs probabilities. They look like…
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Fit KNN regression with k=1 and the prediction curve threads through every training point. Set k=50 and the same curve flattens toward a horizontal line.…
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Two models trained on the same data can look at a new point and give you sharply different answers. Neither one is broken. Each is answering a different…
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Fit a lasso on a dataset with a dozen features and something strange happens. A few coefficients come back as exactly 0.0. Not 0.0031. Not 0.0007. Zero.…
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You fit a line, plot the residuals, and they look like a clean cloud. No funnel, no curve, no obvious outlier. It is tempting to read that plot as a…
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You have probably heard the advice: "Just use a random forest. Trees handle everything." It sounds practical. It is also how many beginners end up with a…
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Most introductions to linear regression make it sound like drawing a line through some dots. That description is true and almost useless. Any line can be…
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Call LinearRegression().fit(X, y) and coefficients appear in milliseconds. The library never shows you the equation it solved, why that equation has a…
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A model that fits is not yet a model you can trust. Fitting takes one line. Trust takes a split, a reading of the numbers, and a look at where the errors…
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You train your first classifier. It hits 85% accuracy on the test set. You feel great—until you realize that a rule which ignores your data entirely would…
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A model that scores 90% accurate can still fail at the exact job it was built for. The headline number looks impressive, but it may be hiding a model that…
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You open a dataset, and the last few columns all look like answers. One column says the fruit is an apple. The next says it is red. A third says it is…
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A coefficient can be unbiased on average and still be wrong in your one dataset. Worse, it can be unbiased and still predict badly, and unbiased and still…
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You build a baseline, get a number, and have no idea whether that number is the best a constant can do or just a habit copied from a tutorial. Here is the…
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A straight line on count data will eventually promise you a negative number of events. The Poisson likelihood with a log link is what stops it.
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A count target is not a permission slip. It is a hypothesis about how the mean and variance move together — and you can test it.
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Fit degree 1 through 9, watch training error fall to almost zero, and pick degree 9. That is the trap. Training error measures how well a model remembers…
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You fit a straight line to data that clearly curves, and the line misses everything. Not by a little—systematically. It sits above the points in one…
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A regression model hands you one number. The person acting on that number needs to know how much weight it can bear. A prediction interval is how you show…
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Your model keeps under-predicting the values you actually care about, and averaging the errors away does not fix it.
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A respectable R² can sit on top of a model that is wrong in a structured way. The score summarizes average fit; it says nothing about where the fit fails.…
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A classification tree asks which split makes the labels purest. A regression tree has no labels to purify — only numbers that scatter. So what is it…
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You have data. You have a prediction goal. And you're stuck on a question that feels like it should be simple: should I use regression or classification?
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Regularization is not a magic switch that makes every model better. It is a deliberate trade: you give up a little accuracy on your training data to gain…
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A regression model reports a solid R-squared, so you call it done. Then you plot the predictions against the actual values and notice something…
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Two features that measure almost the same thing can make ordinary least squares produce coefficients that swing wildly while the predictions barely move.…
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One row can steer a line. Add a single extreme observation to an otherwise clean dataset, refit, and watch the slope tilt — even though every other point…
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A prediction interval built from a model's own residuals has no guarantee at all. Split conformal prediction replaces that hope with a finite-sample…
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One point sits far from the rest of the scatter, and the fitted line visibly tilts toward it. Move that point twice as far away. Squared error answers with…
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You have heard both terms. You can even recite rough definitions. Then you open your own dataset, stare at the columns, and freeze. Is this supervised or…
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Add a product term, watch the validation score tick up, declare victory. I have watched this exact sequence produce a confident conclusion on a dataset…
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