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---
name: discretization
description: "Discretize continuous features with unsupervised, tree-based, or
MDLP splitters before Bayesian-network fitting."
disable-model-invocation: true
metadata:
disco-role: operating
license: NOASSERTION
---
# Discretization
Use this sub-skill when you need to bucket numeric data, prepare features for a Bayesian network, or debug tree-based and MDLP splitters.
## Route here when
- The task names `Discretiser`, `DecisionTreeSupervisedDiscretiserMethod`, or `MDLPSupervisedDiscretiserMethod`.
- You need fixed, uniform, quantile, outlier, or percentile bucketization.
- You need supervised split thresholds from a decision tree or MDLP.
- You need to discretize features before fitting `BayesianNetworkClassifier` or another BN workflow.
## Route elsewhere when
- You need the fitted BN itself -> `../bayesian-networks/SKILL.md`.
- You need the causal DAG learner -> `../structure-learning/SKILL.md`.
- You need synthetic data or feature mapping helpers -> `../synthetic-data/SKILL.md`.
## Start fast
1. Read `references/api-reference.md` for the exact constructor and split-method behavior.
2. Read `references/workflows.md` for the quickest unsupervised, tree-based, MDLP, and BN-classifier examples.
3. Run `../../scripts/smoke_discretizer.py` when install issues or binning behavior look suspicious.
4. Read `references/troubleshooting.md` for method-selection, split-point, and optional-dependency failures.
