Train Classifier (Decision Tree) Node

AI/ML/Classification

Fit/Train a Decision Tree classifier. Native multi-class support with interpretable rules.

fit_decision_treeml

Inputs7

Outputs2

Security exposure6/10

Packageml

Ratings

Scores range from 0 to 10. Higher values mean more impact, exposure, or operational weight.

SecurityAttack surface and exposure impact.

6/10Medium

PrivacyPotential sensitivity of processed data.

6/10Medium

PerformanceRuntime or resource pressure.

8/10High

GovernancePolicy, audit, or compliance impact.

7/10High

ReliabilityOperational stability considerations.

7/10High

CostExternal or compute cost impact.

8/10High

Input Pins

Input

Execution

exec_in

Execution trigger that begins Decision Tree training

Data Source

String

source

Choose which backend supplies the training data

Default Database

Database

Max Depth

Integer

max_depth

Maximum depth of the tree. None means unlimited.

Default 10

Min Samples Split

Integer

min_samples_split

Minimum number of samples required to split a node

Default 2

Split Quality

String

split_quality

Impurity metric that scores candidate splits. Gini is cheaper, Entropy favours balanced information gain.

Default Gini

GiniEntropy

Min Samples Leaf

Float

min_weight_leaf

Minimum number of samples (total sample weight) a split has to place in each leaf

Default 1

Range 0 to 10000

Min Impurity Decrease

Float

min_impurity_decrease

Minimum impurity decrease a split has to bring to be applied. Must be greater than zero; larger values prune harder.

Default 0.00001

Output Pins

Done

Execution

exec_out

Activated once training completes

Model

Struct

model

Thread-safe handle to the trained Decision Tree classifier

Schema enforced

Node Info

Internal name: fit_decision_tree
Category: AI/ML/Classification
Version: 1