hello·ai

Classifying with small models · Module E of 6

Exploring data by hand

What is actually in the data before I label it?

Projection, clustering, neighbours and topic models as tools for looking, plus a labelling workflow that uses them.

Topics in this module

Topics

🔒 Unlocks at 1 of 1 topics in Module C — 0 done.
  1. E1

    Dimensionality reduction Planned

    PCA, t-SNE and UMAP: look, don’t measure.

  2. E2

    Clustering Planned

    K-means versus HDBSCAN; clusters are not labels.

  3. E3

    k-nearest neighbours Planned

    Label by the neighbours’ vote, and use it to audit mislabels.

  4. E4

    Topic modelling with NMF Planned

    Readable word topics from TF-IDF; weak on short text.

  5. E5

    Gaussian mixture models Planned

    Soft clustering via EM, BIC to pick the count, and novelty detection.

  6. E6

    A labelling workflow Planned

    Read raw data, map, guide, seed, scale, audit, lock the test set.