Train Classifier (SVM) Node

T

AI/ML/Classification

Fit/Train Support Vector Machines (SVM) for Multi-Class Classification

fit_svm_multi_classml

Inputs5

Outputs2

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.

6/10Medium

GovernancePolicy, audit, or compliance impact.

6/10Medium

ReliabilityOperational stability considerations.

7/10High

CostExternal or compute cost impact.

7/10High

Input Pins

5

Input

Execution

exec_in

Execution trigger that begins SVM training

Data Source

String

source

Choose which backend supplies the training data

Default Database

Database

Kernel

String

kernel

Feature-space mapping. Gaussian separates non-linear classes, Linear is the plain SVM, Polynomial adds interaction terms.

Default Gaussian

GaussianLinearPolynomial

Kernel Parameter

Float

kernel_param

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

Default 30

Range 0.0001 to 1000

C

Float

c

Penalty for misclassified training rows, applied to both the positive and the negative side. Higher values fit the training data harder and risk overfitting.

Default 1

Range 0.0001 to 100000

Output Pins

2

Done

Execution

exec_out

Activated once training completes

Model

Struct

model

Thread-safe handle to the trained SVM classifier

SchemaNodeMLModelNodeMLModelNodeMLModel1 fields

model_refstringrequired

Schema enforced

Node Info

Internal name

fit_svm_multi_class

Category

AI/ML/Classification

Version

1