Regression Metrics Node

R

AI/ML/Metrics

Regression Metrics

Calculate MSE, RMSE, MAE, and R² for regression predictions

ml_eval_regressionml

Inputs4

Outputs2

Security exposure2/10

Packageml

Ratings

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

SecurityAttack surface and exposure impact.

2/10High

PrivacyPotential sensitivity of processed data.

2/10High

PerformanceRuntime or resource pressure.

2/10High

GovernancePolicy, audit, or compliance impact.

3/10High

ReliabilityOperational stability considerations.

1/10High

CostExternal or compute cost impact.

1/10High

Input Pins

4

Input

Execution

exec_in

Execution trigger to start regression metrics calculation

Database

Struct

database

Database connection containing predictions and actuals

SchemaNodeDBConnectionNodeDBConnectionNodeDBConnection1 fields

cache_keystringrequired

Schema enforced

Predictions Column

String

predictions_col

Column name containing predicted float values

Default prediction

Actuals Column

String

actuals_col

Column name containing actual/true float values

Default target

Output Pins

2

Done

Execution

exec_out

Activated once regression metrics calculation completes

Result

Struct

result

Regression metrics (MSE, RMSE, MAE, R²)

SchemaRegressionMetricsRegressionMetricsRegressionMetrics5 fields

msenumber:doublerequired

Mean Squared Error

format double

rmsenumber:doublerequired

Root Mean Squared Error

format double

maenumber:doublerequired

Mean Absolute Error

format double

r2number:doublerequired

R² coefficient of determination

format double

n_samplesinteger:uintrequired

Number of samples evaluated

format uintmin 0

Node Info

Internal name

ml_eval_regression

Category

AI/ML/Metrics