Disco
Find novel, statistically validated patterns in tabular data — feature interactions, subgroup effects, and conditional relationships that humans and agents miss.
Made by Leap Laboratories.
What it actually does
Most data analysis starts with a question. Disco starts with the data.
Without biases or assumptions, it finds combinations of feature conditions that significantly shift your target column — things like "patients aged 45–65 with low HDL and high CRP have 3× the readmission rate" — without you needing to hypothesise that interaction first.
Each pattern is:
- Validated on a hold-out set — increases the chance of generalisation
- FDR-corrected — p-values included, adjusted for multiple testing
- Checked against academic literature — to help you understand what you've found, and identify if it is novel.
The output is structured: conditions, effect sizes, p-values, citations, and a novelty classification for every pattern found.
Use it when: "which variables are most important with respect to X", "are there patterns we're missing?", "I don't know where to start with this data", "I need to understand how A and B affect C".
