Heuristic:Snorkel team Snorkel Minimum Three LFs
| Knowledge Sources | |
|---|---|
| Domains | Weak_Supervision, Graphical_Models |
| Last Updated | 2026-02-14 21:00 GMT |
Overview
The LabelModel requires at least 3 labeling functions to train. This is a mathematical constraint of the matrix completion approach used to estimate LF accuracies without ground truth labels.
Description
Snorkel's LabelModel uses a generative probabilistic graphical model that learns LF accuracy parameters by exploiting the agreement and disagreement patterns among labeling functions. This approach requires observing enough pairwise statistics between LFs to solve the underlying system of equations. With fewer than 3 LFs, the system is underdetermined.
Usage
Always ensure you have at least 3 labeling functions before calling `LabelModel.fit()`. If you have only 1-2 LFs, use the MajorityLabelVoter baseline instead (which does not learn accuracy parameters).
The Insight (Rule of Thumb)
- Action: Provide at least 3 labeling functions in the label matrix passed to `LabelModel.fit()`.
- Value: Minimum `m >= 3` where `m` is the number of LF columns in `L_train`.
- Trade-off: None -- this is a hard mathematical constraint, not a tunable parameter. With fewer than 3 LFs, the model simply cannot be trained.
- Alternative: For 1-2 LFs, use `MajorityLabelVoter` or `MajorityClassVoter` from `snorkel.labeling.model.baselines`.
Reasoning
The LabelModel estimates LF accuracies by solving a system of equations derived from observable LF overlap statistics. With `m` LFs, there are `m*(m-1)/2` pairwise overlap statistics. The model needs to estimate `m` accuracy parameters. For `m=2`, there is only 1 pairwise statistic but 2 unknowns -- the system is underdetermined. For `m=3`, there are 3 pairwise statistics and 3 unknowns, which is the minimum for a unique solution.
Code evidence from `label_model.py:596-597`:
if self.m < 3:
raise ValueError("L_train should have at least 3 labeling functions")