Train Classifier (K-Nearest Neighbours) Node

AI/ML/Classification

Fit a K-Nearest-Neighbours classifier. Non-parametric and instance based: the fitted model embeds a verbatim copy of the whole training set instead of learned coefficients, so every training row (and any personal data in it) travels with the model, is written into every saved model file and can be reconstructed by anyone holding it. Treat the model with the same care as the source table.

fit_knn_classifierml

Inputs4

Outputs2

Security exposure3/10

Packageml

Ratings

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

SecurityAttack surface and exposure impact.

3/10Low

PrivacyPotential sensitivity of processed data.

2/10Low

PerformanceRuntime or resource pressure.

4/10Medium

GovernancePolicy, audit, or compliance impact.

2/10Low

ReliabilityOperational stability considerations.

7/10High

CostExternal or compute cost impact.

4/10Medium

Input Pins

Input

Execution

exec_in

Execution trigger that begins KNN training

Data Source

String

source

Choose which backend supplies the training data

Default Database

Database

Neighbours (k)

Integer

How many nearest training rows vote on each prediction. Must be at least 1 and cannot exceed the number of training rows. Larger values smooth the decision boundary.

Default 5

Range 1 to 1000

Distance Weighted

Boolean

distance_weighted

Weight each neighbour by the inverse of its distance instead of counting every neighbour equally. Helps when k is large or classes overlap.

Default false

Output Pins

Done

Execution

exec_out

Activated once the training set has been validated and embedded

Model

Struct

model

Thread-safe handle to the trained KNN classifier. Contains a full copy of the training set.

Schema enforced

Node Info

Internal name: fit_knn_classifier
Category: AI/ML/Classification