---

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.