Evaluating a Regression Model: How Do We Know Our Predictions Are Good?

2026-07-19 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Novice, long

Summary

This article details the evaluation of a Simple Linear Regression model built to predict salary based on years of experience, using a dataset of 30 rows split into 24 for training and 6 for testing. It emphasizes that a model's output doesn't inherently mean it's good, necessitating rigorous evaluation. The analysis compares actual versus predicted values, explains prediction errors (residuals), and introduces key regression metrics. These include Mean Absolute Error (MAE), which for this model is 6286.45, representing the average prediction error. Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), calculated at 49830096.86 and 7059.04 respectively, penalize larger errors more severely. The R² Score, at 0.9024, indicates the model explains 90.24% of salary variation. The article also highlights the importance of visual evaluation through actual vs. predicted and residual plots, and warns against common beginner mistakes like using "accuracy" for regression or relying on a single metric.

Key takeaway

For Data Scientists or Machine Learning Engineers evaluating regression models, you must move beyond simply generating predictions. Instead, rigorously assess model performance by calculating MAE, RMSE, and R² Score, and critically examine residual plots. This multi-metric approach helps you understand both the average error magnitude and how well your model explains data variation, ensuring you build trustable systems rather than just functional ones.

Key insights

Effective regression model evaluation requires understanding prediction errors and applying multiple metrics and visualizations.

Principles

• Model output does not imply quality.
• Evaluate on unseen test data.
• Multiple metrics provide a holistic view.

Method

After training, compare actual vs. predicted values, calculate residuals, then apply MAE, MSE, RMSE, and R² Score. Supplement numerical metrics with actual vs. predicted and residual plots for visual error analysis.

In practice

• Calculate MAE for average error.
• Use RMSE to penalize large errors.
• Plot residuals to detect patterns.

Topics

• Regression Model Evaluation
• Mean Absolute Error
• Root Mean Squared Error
• R² Score
• Residual Analysis
• Linear Regression

Code references

• arpan-artisan/ML-Series

Best for: AI Student, Data Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.