Data quality and labels

This question captures common issues in real datasets.
You can select multiple items.

If you pick “Limited labels”, it often makes sense to also consider “transfer / self-supervised” methods.

Options in this tool

Missing data

Some features/measurements are absent (or entire samples are missing parts).
Common fixes: imputation, models that handle missingness, careful data checks.

Sparse data

Most entries are zero/empty/non-existent (common in high-dimensional tabular, NLP counts, recommender systems).
Common fixes: sparse models, embeddings, regularisation, feature selection.

Noisy data

Inputs are noisy (blur, artefacts, background clutter), measurements have systematic errors, sensor drift, or varying quality. Common fixes: augmentation, denoising, robust training, preprocessing.

Noisy or Limited labels

Labels are imperfect, inconsistent, or you have few labelled samples (even if unlabelled data is large). Common fixes: label cleaning, noise-robust losses, weak supervision, learning with noisy labels, transfer learning, self-supervision, active learning, semi-supervised learning.

Distributed / federated

Data is split across locations and cannot be centralised.
Common fixes: federated learning, secure aggregation, careful evaluation per site.

Class imbalance

Some classes are rare.
Common fixes: reweighting, resampling, focal loss, better metrics, careful splits.