Apply dimensionality reduction
Reduce high-dimensional data
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Write Python code to reduce the dimensionality of a feature matrix X with [number] features using PCA, t-SNE, and UMAP. Determine the optimal number of PCA components using explained variance (95% threshold), visualize t-SNE and UMAP embeddings colored by [label column], and compare clustering quality.
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Pense à standardiser `X` avant d’appliquer PCA/t-SNE/UMAP — sans normalisation, la variance expliquée et les projections peuvent être dominées par les échelles. Pour t-SNE et UMAP, fixe `perplexity` et `n_neighbors` autour de 5-50 selon la taille de tes données ; une valeur trop basse ou haute dégrade la structure. Enfin, utilise `DBSCAN` sur les réductions pour comparer objectivement la qualité du clustering plutôt que de se fier uniquement aux visuels.
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