Train Regressor (Ridge/Lasso/ElasticNet) Node

T

AI/ML/Regression

Fit/Train a penalized linear regression model. Ridge shrinks all coefficients, Lasso drives irrelevant ones to exactly zero (feature selection), Elastic Net mixes both.

fit_elastic_netml

Inputs8

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.

7/10High

GovernancePolicy, audit, or compliance impact.

8/10High

ReliabilityOperational stability considerations.

7/10High

CostExternal or compute cost impact.

8/10High

Input Pins

8

Input

Execution

exec_in

Execution trigger that begins regression training

Data Source

String

source

Choose which backend supplies the training data

Default Database

Database

Penalty Type

String

penalty_type

Ridge = pure L2 (keeps all features, handles correlated ones well), Lasso = pure L1 (zeroes out weak features), ElasticNet = a blend controlled by L1 Ratio

Default ElasticNet

ElasticNetRidgeLasso

Penalty (Alpha)

Float

penalty

Overall regularization strength. 0 means ordinary least squares, larger values shrink the coefficients harder.

Default 1

Range 0 to 1000

L1 Ratio

Float

l1_ratio

Share of the penalty spent on L1 vs L2. Only used when Penalty Type is ElasticNet; Ridge forces 0.0 and Lasso forces 1.0.

Default 0.5

Range 0 to 1

Fit Intercept

Boolean

with_intercept

Fit a bias term. Disable only when the data is already centered.

Default true

Max Iterations

Integer

max_iterations

Upper bound on coordinate descent passes. The solver stops silently at this cap, so a convergence warning is logged when it is hit.

Default 1000

Range 1 to 1000000

Tolerance

Float

tolerance

Convergence tolerance for coordinate descent. Smaller values give a tighter fit at the cost of more iterations.

Default 0.0001

Range 1e-12 to 1

Output Pins

3

Done

Execution

exec_out

Activated once training completes

Model

Struct

model

Thread-safe handle to the trained penalized regression model

SchemaNodeMLModelNodeMLModelNodeMLModel1 fields

model_refstringrequired

Schema enforced

Coefficients

Struct

coefficients

Fitted coefficients and intercept. With Lasso, coefficients that are exactly zero mark features the model discarded.

SchemaLinearCoefficientsLinearCoefficientsLinearCoefficients3 fields

coefficientsArray<number:double>required

Feature coefficients (one per input dimension)

itemsnumber:doublearray item

format double

interceptnumber:doublerequired

The y-intercept (bias term)

format double

n_featuresinteger:uintrequired

Number of input features

format uintmin 0

Node Info

Internal name

fit_elastic_net

Category

AI/ML/Regression