Explore how different models turn inputs into predictions, from linear and logistic methods to neighbors, SVMs, and probabilistic classifiers.
Tutorials
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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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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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A logistic regression prediction is not a label. It is a probability—and the label is a decision you make on top of that probability.
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You can fit a logistic regression in one line and read its coefficients in the next. What almost nobody shows you is where the loss function comes from.…
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You call predict(), and you get back a tidy row of zeros and ones. Clean. Decisive. And completely silent about the thing you actually wanted to see: the…
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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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Most machine learning models are hoarders. They keep every training point around and let each one vote on the prediction. A support vector machine is the…
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Most explanations of support vector machines stop at the picture: two clouds of points, a line between them, a gap on either side. That picture is correct,…
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Same data. Same SVC call. One run scores 0.85, the next 0.55, and the only thing that changed was whether you scaled the features first.
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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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Most classifiers earn their keep by capturing relationships between features. Naive Bayes does the opposite: it assumes those relationships don't exist.…
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The intuition article told you Naive Bayes "combines prior and evidence." Then you tried to write $P(x_1, x_2, \dots, x_n \mid y)$ for real data, and the…
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The most dangerous bug in a beginner text classifier is not in the model. It is in the order of operations. Fit your vectorizer on the whole dataset before…
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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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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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Most people assume multiclass classification is just binary classification with more labels. It is not. The real problem is that many classical models only…
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Naive Bayes and logistic regression can classify the same dataset with nearly the same accuracy, yet they behave very differently on small samples, missing…
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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 classification tutorials teach you to pick one answer. A news article is politics or finance. A movie is action or comedy. A support ticket is billing…
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A rating scale looks like it should be either a multiclass problem or a regression problem. Both instincts quietly discard the information that matters…
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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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You can write theta = theta - lr * grad from memory and still freeze when someone asks where grad came from. That gap is the whole problem. The update rule…
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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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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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Every scikit-learn tutorial shows you the same two lines. Almost none of them stop to show you what changed inside the object between them.
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Same data. Same code. Two accuracy scores that disagree, and nothing in the output explains why.
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A model can score 94% accuracy and still be quietly failing one class. Here is how to see it.
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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 multiclass model hands you K raw scores. The obvious move — divide each by their sum — collapses the first time a score goes negative. Here is the fix,…
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A satisfaction rating of 4 when the truth was 5 is not the same kind of mistake as predicting 1. Most models cannot tell the difference. This one can — but…
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The primal SVM asks for a weight vector in feature space. The trained model never hands you one. It hands you a sum over training points instead — and that…
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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 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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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 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 have your features. You have your target. You are about to reach for a classifier — and then you freeze on a question that feels too basic to ask out…
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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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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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You fit LinearDiscriminantAnalysis, plot the boundary, and get a straight line. Then someone asks why it is straight, and the honest answer is "because the…
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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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