Top t-SNE pitfalls to avoid.
hardAnswer
- (1) Cluster sizes in the map are not meaningful — t-SNE inflates dense clusters.
- (2) Inter-cluster distances are NOT preserved — 'far' clusters aren't necessarily far in data space.
- (3) Random-seed sensitivity → run multiple times.
- (4) Only meaningful in 2/3D.
- (5) NEVER feed t-SNE embedding into a downstream model — use PCA or UMAP instead.
- Wattenberg's 'How to Use t-SNE Effectively' is required reading.
Check yourself — multiple choice
- Distances are meaningful
- Cluster sizes / inter-cluster distances NOT meaningful; seed-sensitive; only 2/3D; NEVER feed into downstream model — use UMAP/PCA
- Random
- Only for regression
t-SNE pitfalls: sizes/distances not meaningful; never as ML input.
#dimensionality-reduction#visualization
Practise Unsupervised Learning
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