Changing feature magnitudes or units and reasoning about the resulting effects on geometry, optimization, and model behavior.
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20 articles in this tag.
Anomaly detection is not "find the weird rows." It is a decision about what counts as normal, made before you ever run an algorithm. Get that decision…
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Clustering always returns groups. Even on random noise, even on data with no structure worth naming, every algorithm will happily partition your points and…
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K-means does not discover the real categories hiding in your data. It draws geometric boundaries around points that happen to sit close together. Those…
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You learned K-means, and it felt clean: pick a number of groups, let centroids pull points inward, and read off the labels. Then you hit a dataset with…
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Change one number in a DBSCAN call and the whole story of your data can flip: two clusters become one, or a clean grouping dissolves into a field of noise.…
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Two houses sit side by side in your table. One is 1,800 square feet with 3 bedrooms. The other is 2,000 square feet with 5 bedrooms. To a k-NN model, the…
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You train the same model twice on the same data. The only difference: you scaled the features before the second run. The results are not slightly…
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Most people assume a machine learning model reads a spreadsheet the way a person does—scanning row by row, comparing values column by column. It does not.…
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A model starts with random parameters and ends up making sharp predictions. Nobody tells it the answer. It just keeps nudging itself in the right direction…
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You fit the model, plot a tidy dendrogram, cut it at the biggest gap, and report three clusters. Then you change one argument — the linkage — and get a…
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Most beginners ask the wrong question first. They ask, "Which clustering algorithm is better?" The real question is, "Which question am I actually trying…
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Most people stop at fit(). They get a label array, scatter-plot it, and call the job done. But the label array is not the result — it is the beginning of…
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Same data. Same code. Two accuracy scores that disagree, and nothing in the output explains why.
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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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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 people assume PCA "removes unimportant features." It does not. It rotates your data into new directions ranked by variance, then drops the quietest…
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You fit PCA, print the explained variance ratio, and see that PC1 captures 92% of the variance. That looks like a strong result. It might be an artifact of…
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You can call PCA(), read explained_variance_ratio_, and get a working result. Then someone asks why the principal directions are eigenvectors of the…
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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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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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