MAEEvaluator

MAEEvaluator

Calculates Mean Absolute Error (MAE) between predictions and actual values.

Formula

MAE = (1/n) × Σ|yᵢ - ŷᵢ|

Where:

• yᵢ = actual value
• ŷᵢ = predicted value
• n = number of observations

Basic Usage

from epftoolbox2.evaluators import MAEEvaluator

evaluator = MAEEvaluator()

In Pipeline

from epftoolbox2.pipelines import ModelPipeline
from epftoolbox2.models import OLSModel
from epftoolbox2.evaluators import MAEEvaluator
from epftoolbox2.exporters import TerminalExporter

pipeline = (
    ModelPipeline()
    .add_model(OLSModel(predictors=predictors, name="OLS"))
    .add_evaluator(MAEEvaluator())
    .add_exporter(TerminalExporter())
)

report = pipeline.run(...)
print(report.summary())
#    model       MAE
# 0    OLS  26.0199

Creating Custom Evaluators

See Extending for how to create custom evaluators like RMSE or MAPE.