Interval Calibration for Regression

Conformal prediction provides uncertainty estimates, generating prediction intervals for regressors and confidence sets for classifiers. This ensures that model predictions include a measure of reliability, particularly important for risk-sensitive applications. See details in split_conformal.

The calibrate_interval method allows users to fit an interval model for conformal prediction.

1. Prepare data and model

from modeva import DataSet
from modeva.models import MoXGBRegressor

ds = DataSet()
ds.load(name="BikeSharing")
ds.set_random_split()

model = MoXGBRegressor(max_depth=2)
model.fit(ds.train_x, ds.train_y)

2. Calibration

model.calibrate_interval(X_test, y_test, alpha=0.1)

The predict_interval method then applies the calibrated prediction intervals.

3. Get calibrated prediction intervals

Conformal prediction assumes that the calibration data is representative of the test distribution. If the data distribution shifts significantly, recalibration may be necessary.

Examples

Example: Conformal Prediction Calibration for Regression

Calibrating Regressor Prediction Interval