When is quantile regression more useful than mean (OLS) regression?
hardAnswer
- Quantile regression models a specific conditional quantile of y (e.g., the median, 90th percentile) instead of the mean.
- Use it when (1) you care about tails, not the average — delivery time SLA, inventory sizing, risk quantiles; (2) the target distribution is asymmetric or heavy-tailed; (3) you want a prediction interval by fitting several quantiles.
- It minimizes the pinball (quantile) loss instead of MSE.
Check yourself — multiple choice
- Quantile regression predicts probabilities
- It models specific quantiles (median, 90th percentile) instead of the mean — useful for tails and asymmetric distributions
- It's the same as logistic regression
- It can only predict the median
Quantile regression models any conditional quantile via pinball loss — perfect for tails/SLAs.
#linear-regression#metrics-regression
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