Representing data with fewer dimensions through projection, components, embeddings, or other compression while considering information and interpretation tradeoffs.
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9 articles in this tag.
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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You add more columns to your dataset expecting a better model. Instead, your k-nearest neighbors accuracy drops, your clusters turn to mush, and every…
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Feature hashing trades a little information and a lot of interpretability for something genuinely rare in machine learning: a categorical encoding that…
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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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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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You have a wide table of features, a model to build, and a quiet suspicion that half those columns are noise. The instinct is to shrink the dataset. But…
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You run PCA on your data and get one picture. You run t-SNE on the same data and get a completely different one. One shows two messy blobs. The other shows…
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