Have you been asked to provide prediction intervals besides the mean predictions? Prediction intervals have many use cases because they provide the range of the predicted values to give better guidance. In financial risk management. the prediction intervals for the high range can help risk managers to mitigate risks. In science, a predicted life of a battery between 100 to 110 hours can inform users when to take actions. While an ordinary least square (OLS) regression predicts the mean, Quantile regression (QR) can provide the intervals that a future value will fall. For this reason, QR has received increasing attention and applied to many areas such as investment, finance, economics, medicine and engineering.

The genesis of the idea is simple: if OLS models the conditional means, why don’t we model the conditional median or other percentiles? The term quantile in QR is the same as percentile. It is interesting to know that QR was invented about the same time as the ordinary least square (OLS), but becomes popular due to today’s good computational power.

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A Tutorial on Quantile Regression, Quantile Random Forests, and Quantile GBM
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