Regression Metrics and Residual Analysis
Choose between MAE, RMSE and friends, then read residuals to find what your model misses
A taste of a lesson
My delivery time model has MAE of 6 minutes but RMSE of 15. What does the gap tell me?
It tells you most deliveries are predicted fairly well, but a minority have very large errors. RMSE squares each error before averaging, so a few 40 or 50 minute misses pull it up much more than they move MAE. Next step: sort by absolute error and look at the worst 20 cases. Are they a certain area, time of day, or weather? Often they reveal a missing feature or bad data. Before looking, what is your guess for what those big misses have in common?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Calculate MAE, RMSE and R squared and explain each in plain words
- Choose a metric that matches the cost of errors
- Avoid the traps of percentage error metrics
- Read residual plots to find missing patterns
- Break errors down by segment and compare to a baseline
Lesson plan
- 1 Errors in the target's units Compute residuals and MAE and describe them in real units. Start
- 2 Squared errors and RMSE See how RMSE reacts to large errors compared with MAE. Start
- 3 R squared and its limits Interpret R squared correctly on training and test data. Start
- 4 Percentage errors Understand when MAPE misleads and what to use instead. Start
- 5 Reading residual plots Diagnose missing patterns from the shape of residuals. Start
- 6 Errors by segment Find groups and cases where the model performs badly. Start
Try asking
About this tutor
A beginner tutor for anyone predicting numbers: prices, demand, durations or scores. You will calculate MAE, RMSE and R squared by hand, learn which one matches your situation, and see the traps in percentage errors. Most of the time goes on residuals, the gaps between actual and predicted values: plotting them, finding patterns, checking errors by segment and comparing against a simple baseline. You finish able to explain a regression model's quality to a non technical colleague and to spot where it needs better features.
Reviews
4.0
3 ratingsSample
- Kaito N.Sample
Good content, though I wanted more on forecasting metrics specifically. Fine for general regression.
- Claire B.Sample
Helpful on residual plots. I had a funnel shape and the log target suggestion fixed most of it.
- Arjun V.Sample
The 2, 2, 2, 2, 22 example made the MAE versus RMSE difference obvious. Segment tables are now part of every report I send.
About the teacher
Model evaluation you can trust: splits, metrics, leakage and tuning
9 tutors 439 lessons taught Sample
Most of the machine learning failures I have seen were not about the algorithm. They came from a test set that was used too often, a feature that leaked the answer, or a metric that hid the real cost of mistakes. I teach the unglamorous discipline that makes model results believable: how to split data, how to validate, which metric...
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