Principle:Evidentlyai Evidently Regression Quality Evaluation

Knowledge Sources	Evidently Regression Evidently
Domains	ML_Evaluation, Regression
Last Updated	2026-02-14 12:00 GMT

Overview

A comprehensive regression model quality evaluation mechanism that computes standard error metrics and optional diagnostic visualizations.

Description

Regression Quality Evaluation assesses regression model performance via: Mean Error, MAPE, RMSE, MAE, R2 Score, and Absolute Max Error. It optionally generates predicted-vs-actual plots, error plots, and error distributions.

Usage

Use when evaluating regression models. Requires DataDefinition with regression task configuration.

Theoretical Basis

Standard regression metrics:

$MAE = \frac{1}{n} \sum_{i = 1}^{n} | y_{i} - {\hat{y}}_{i} |$

$RMSE = \sqrt{\frac{1}{n} \sum_{i = 1}^{n} (y_{i} - {\hat{y}}_{i})^{2}}$

$R^{2} = 1 - \frac{\sum (y_{i} - {\hat{y}}_{i})^{2}}{\sum (y_{i} - \bar{y})^{2}}$

Related Pages

Implemented By

Implementation:Evidentlyai_Evidently_RegressionQuality_Preset

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