Conditional conformal prediction intervals

Patent metadata

Field	Value
Publication number	US-2025272578-A1
Application number	US-202418586484-A
Country	US
Kind code	A1
Filing date	Feb 25, 2024
Priority date	Feb 25, 2024
Publication date	Aug 28, 2025
Grant date	—

Abstract

An apparatus for computing a conditional conformal prediction interval for a machine learning point prediction regression model and calibration point predictions forming a distribution of an error around the point prediction regression model in an input space. The apparatus includes a conformal regions circuit configured to compute a quantile regression of the error to compute an approximation of a quantile of the error. The conformal regions circuit is further configured to identify a set of regions in the input space where the distribution within each region in the set of regions is interpretably constant. In one embodiment, the apparatus also includes a conformal prediction circuit configured to compute the conditional conformal prediction interval for the point prediction regression model conditioned on the identified set of regions and the corresponding computed quantile of the error for each region in the set of regions.

First claim

What is claimed:

1 . A computer-implemented method for computing a conditional conformal prediction interval for a machine learning point prediction regression model and calibration point predictions forming a distribution of an error around the point prediction regression model in an input space, the method comprising:
computing a quantile regression of the error to compute an approximation of a quantile of the error; identifying a set of regions in the input space where the distribution within each region in the set of regions is interpretably constant; and computing the conditional conformal prediction interval for the point prediction regression model conditioned on the identified set of regions and the corresponding computed quantile of the error for each region in the set of regions.

2 . The computer-implemented method of claim 1 , wherein the computing a quantile regression of the error comprises minimizing pinball loss.

3 . The computer-implemented method of claim 1 , wherein the computing a quantile regression of the error comprises computing conformity scores for the calibration point predictions.

4 . The computer-implemented method of claim 3 , wherein the computing conformity scores comprises computing individual conformity scores for the calibration point predictions.

5 . The computer-implemented method of claim 3 , wherein the computing conformity scores comprises computing absolute error of the calibration point predictions.

6 . The computer-implemented method of claim 1 , wherein the computing a conditional conformal prediction interval comprises adding the computed quantile of the error corresponding to a first region in the set of regions to the point prediction regression model and subtracting the computed quantile of the error corresponding to the first region from the point prediction regression model.

7 . The computer-implemented method of claim 6 , wherein a first computed quantile of the error corresponding to the first region and a second computed quantile of the error corresponding to a second region in the set of regions are different based on a predetermined confidence level.

8 . The computer-implemented method of claim 7 , wherein the identifying the first and second regions comprises using a machine learning decision tree regression model.

9 . An apparatus for computing a conditional conformal prediction interval for a machine learning point prediction regression model and calibration point predictions forming a distribution of an error around the point prediction regression model in an input space, the apparatus comprising:
a conformal regions circuit configured to:
compute a quantile regression of the error to compute an approximation of a quantile of the error;
identify a set of regions in the input space where the distribution within each region in the set of regions is interpretably constant; and
a conformal prediction circuit configured to compute the conditional conformal prediction interval for the point prediction regression model conditioned on the identified set of regions and the corresponding computed quantile of the error for each region in the set of regions.

10 . The apparatus of claim 9 , wherein the computing a quantile regression of the error comprises computing conformity scores for the calibration point predictions.

11 . The apparatus of claim 9 , wherein the conformal regions circuit is configured to compute the quantile regression of the error based on minimizing pinball loss.

12 . The apparatus of claim 10 , wherein the conformal regions circuit is configured to compute individual conformity scores for the calibration point predictions.

13 . The apparatus of claim 10 , wherein the conformal regions circuit is configured to compute conformity scores based on absolute error of the calibration point predictions.

14 . The apparatus of claim 9 , wherein the conformal prediction circuit is configured to add the computed quantile of the error corresponding to a first region in the set of regions to the point prediction regression model and to subtract the computed quantile of the error corresponding to the first region from the point prediction regression model.

15 . The apparatus of claim 14 , wherein a first computed quantile of the error corresponding to the first region and a second computed quantile of the error corresponding to a second region in the set of regions are different based on a predetermined confidence level.

16 . The apparatus of claim 15 , wherein the conformal regions circuit is configured to identify the first and second regions based on a machine learning decision tree regression model.

17 . A computer system for computing a conditional conformal prediction interval for a machine learning point prediction regression model and calibration point predictions forming a distribution of an error around the point prediction regression model in an input space, the computer system having a processor, a computer-readable memory, a computer-readable tangible storage device, and program instructions stored on the storage device for execution by a processor via the computer-readable memory, wherein the computer system is configured to perform a method, comprising:
computing a quantile regression of the error to compute an approximation of a quantile of the error; identifying a set of regions in the input space where the distribution within each region in the set of regions is interpretably constant; and computing the conditional conformal prediction interval for the point prediction regression model conditioned on the identified set of regions and the corresponding computed quantile of the error for each region in the set of regions.

18 . The computer system of claim 17 , wherein the computing a quantile regression of the error comprises computing conformity scores for the calibration point predictions.

19 . The computer system of claim 17 , wherein the computing a quantile regression of the error comprises minimizing pinball loss.

20 . The computer system of claim 18 , wherein the identifying a set of regions comprises using a machine learning decision tree regression model.

Assignees

• IBM

Inventors

• Martinez Gil Natalia Lucienne
• Patel Dhavalkumar C
• Reddy Chandrasekhara K
• Kalagnanam Jayant R

Classifications

• G06N3/08

  Learning methods · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06N20/20

  Ensemble learning · CPC title

• G06N20/00

  Machine learning · CPC title

• G06N5/01 Primary

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title