Lace is a Bayesian Tabular inference engine for rust and python designed to facilitate scientific discovery by learning a model of the data instead of a model of a question.
The v0.7.0 release of Lace adds variability calculations along with performance improvements and better error handling
You can now calculate the variability of a conditional distribution. "Variability" is variance for target types with defined mean and variance and is entropy otherwise.
For more information on this release, see the CHANGELOG.
Main page: https://lace.dev
What is Lace?
Lace ingests pseudo-tabular data from which it learns a joint distribution over the table, after which users can ask any number of questions and explore the knowledge in their data with no extra modeling. Lace is both generative and discriminative, which allows users to
- determine which variables are predictive of which others
- predict quantities or compute likelihoods of any number of features conditioned on any number of other features
- identify, quantify, and attribute uncertainty from variance in the data, epistemic uncertainty in the model, and missing features
- generate and manipulate synthetic data
- identify anomalies, errors, and inconsistencies within the data
- determine which records/rows are similar to which others on the whole or given a specific context
- edit, backfill, and append data without retraining