Accuracy Node

A

AI/ML/Metrics

Accuracy

Calculate classification accuracy by comparing predictions to actual values

ml_eval_accuracyml

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 accuracy 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 values

Default prediction

Actuals Column

String

actuals_col

Column name containing actual/true values

Default target

Output Pins

2

Done

Execution

exec_out

Activated once accuracy calculation completes

Result

Struct

result

Accuracy metrics including score and counts

SchemaAccuracyMetricsAccuracyMetricsAccuracyMetrics3 fields

accuracynumber:doublerequired

Accuracy score (0.0 to 1.0)

format double

correct_countinteger:uintrequired

Number of correct predictions

format uintmin 0

total_countinteger:uintrequired

Total number of predictions

format uintmin 0

Node Info

Internal name

ml_eval_accuracy

Category

AI/ML/Metrics