Fit Novelty Detection (One-Class SVM) Node

AI/ML/Classification

Fit a One-Class SVM on normal observations only. Predictions flag whether a new row is an inlier (1) or an outlier (0).

fit_one_class_svmml

Inputs6

Outputs3

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.

4/10Medium

GovernancePolicy, audit, or compliance impact.

5/10Medium

ReliabilityOperational stability considerations.

6/10Medium

CostExternal or compute cost impact.

5/10Medium

Input Pins

Input

Execution

exec_in

Execution trigger that begins One-Class SVM training

Data Source

String

source

Choose which backend supplies the training data

Default Database

Database

Nu

Float

Upper bound on the fraction of training rows the model is allowed to treat as outliers, in (0, 1]. Raise it when the training set is known to be contaminated.

Default 0.1

Range 0.0001 to 1

Kernel

String

kernel

Feature-space mapping. Gaussian wraps a tight non-linear boundary around the data, Linear yields a half-space, Polynomial adds interaction terms.

Default Gaussian

GaussianLinearPolynomial

Kernel Parameter

Float

kernel_param

Gaussian: the eps in exp(-||x - x'||^2 / eps), larger means a looser boundary. Polynomial: the degree of (<x, x'> + 1)^degree. Ignored for Linear.

Default 30

Range 0.0001 to 1000

Solver Tolerance

Float

tolerance

Stopping threshold of the SMO solver. Smaller values train longer for a more precise boundary.

Default 0.001

Range 1e-7 to 1

Output Pins

Done

Execution

exec_out

Activated once training completes

Model

Struct

model

Thread-safe handle to the trained One-Class SVM

Schema enforced

Support Vectors

Integer

support_vectors

Number of training rows that define the learned boundary

Node Info

Internal name: fit_one_class_svm
Category: AI/ML/Classification