Apply Transform Node

A

AI/ML/Preprocessing

Apply a fitted transformer (Feature Scaler, TF-IDF) to a table, writing one vector per row. A Feature Scaler replays the exact offsets and scales learned at fit time, so applying it to train and test gives both the same statistics. TF-IDF is different: linfa recomputes the inverse document frequencies from the table being transformed, so vectors are only comparable within a single Apply Transform run.

ml_apply_transformml

Inputs4

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.

6/10Medium

GovernancePolicy, audit, or compliance impact.

6/10Medium

ReliabilityOperational stability considerations.

7/10High

CostExternal or compute cost impact.

6/10Medium

Input Pins

4

Input

Execution

exec_in

Execution trigger that starts the transform

Model

Struct

model

Fitted transformer to apply. Classifiers and regressors belong on the Predict node.

SchemaNodeMLModelNodeMLModelNodeMLModel1 fields

model_refstringrequired

Schema enforced

Data Source

String

source

Choose which backend supplies the rows to transform

Default Database

Database

Batch Size

Integer

batch_size

Number of records to transform per batch (default: 5000, 0 = process all at once)

Default 5000

Output Pins

1

Done

Execution

exec_out

Activated once every batch is transformed and written

Node Info

Internal name

ml_apply_transform

Category

AI/ML/Preprocessing