Silhouette Score Node

AI/ML/Metrics

Evaluate clustering quality: how much closer each sample sits to its own cluster than to the nearest other one (-1 to +1)

ml_silhouette_scoreml

Inputs5

Outputs4

Security exposure8/10

Packageml

Ratings

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

SecurityAttack surface and exposure impact.

8/10High

PrivacyPotential sensitivity of processed data.

8/10High

PerformanceRuntime or resource pressure.

4/10Medium

GovernancePolicy, audit, or compliance impact.

7/10High

ReliabilityOperational stability considerations.

8/10High

CostExternal or compute cost impact.

5/10Medium

Input Pins

Input

Execution

exec_in

Execution trigger that begins the silhouette evaluation

Database

Struct

database

Database connection containing the feature vectors and their cluster assignments

Schema enforced

Feature Col

String

features_col

Column holding the feature vectors the clustering was computed on. Distances are euclidean, so scale the features first if their ranges differ.

Default vector

Cluster Col

String

labels_col

Column holding the cluster assignment of each sample, as a string name or a non-negative integer id

Default cluster

Max Samples

Integer

max_samples

Upper bound on the samples used. The metric compares every sample with every other one, so the cost grows quadratically; larger sets are sub-sampled evenly.

Default 2000

Range 2 to 20000

Output Pins

Done

Execution

exec_out

Activated once the silhouette evaluation completes

Score

Float

score

Mean silhouette score across all evaluated samples (-1 to +1, higher is better)

Samples

Integer

n_samples

Number of samples the score was computed on after sub-sampling

Clusters

Integer

n_clusters

Number of distinct clusters found in the cluster column

Node Info

Internal name: ml_silhouette_score
Category: AI/ML/Metrics