Stratified Split Node

AI/ML/Dataset

Split a dataset into training and testing subsets, keeping every class at its original proportion in both subsets

ai_ml_dataset_stratified_splitml

Inputs7

Outputs1

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.

5/10Medium

PerformanceRuntime or resource pressure.

5/10Medium

GovernancePolicy, audit, or compliance impact.

6/10Medium

ReliabilityOperational stability considerations.

7/10High

CostExternal or compute cost impact.

6/10Medium

Input Pins

Input

Execution

exec_in

Execution trigger that starts the stratified split

Split Ratio

Float

split

Share of each class that goes to the training set (rest goes to test). Must be between 0 and 1, exclusive

Default 0.8

Range 0 to 1

Label Column

String

label_column

Name of the column containing class labels for stratification

Default label

Seed

Integer

seed

Seed for the per-class shuffle. Any non-zero value makes the split reproducible; 0 draws a fresh seed each run and logs it

Default 0

Data Source

Struct

source

Data Source (DB or CSV)

Schema enforced

Training Database

Struct

train

Destination database that receives the training rows. It is cleared before every run

Schema enforced

Test Database

Struct

test

Destination database that receives the testing rows. It is cleared before every run

Schema enforced

Output Pins

Done

Execution

exec_out

Activated once the stratified split has finished

Node Info

Internal name: ai_ml_dataset_stratified_split
Category: AI/ML/Dataset
Version: 1