ตัวชี้วัดการถดถอย: MAE, MSE, R2

When a model predicts numbers, you need one honest score for how far off it is. That score is an error metric, your truth-teller. 📏

Start with the residual: the gap between the true value and the predicted one. Every regression metric is just a way to summarize these gaps.

MAE takes the size of each error, ignores the sign, and averages them. It tells you the typical miss in the same units as your target.

Because MAE stays in real units, you can say the model is off by about 5 dollars on average. Stakeholders love that plain reading.

MSE squares each error before averaging. Squaring punishes big misses much harder, so a few large errors dominate the score.

One catch: MSE is in squared units, so dollars become squared dollars. The number is hard to interpret on its own.

Take the square root of MSE to get RMSE, back in normal units. It keeps the heavy penalty on big errors but reads cleanly.

Choose MAE when every error matters equally, and RMSE when large mistakes are especially costly and you want them penalized more.

R2 asks a different question: what fraction of the variation in the target your model explains. Higher means a better fit.

An R2 of 1.0 is perfect, 0.0 is no better than guessing the mean. It can even go negative when a model fits worse than that.

No single number tells the whole story. Pair an error metric like RMSE with R2 to see both the typical miss and the overall fit.

Let's pin down what each regression metric really measures.

Use MAE for plain average error, RMSE to punish big misses, and R2 for fraction of variance explained. Read them together. 🎯