All regression examples

Following is a collection of notebooks demonstrating how to use MAPIE.

1. Quickstart

The following examples present the main functionalities of MAPIE through basic quickstart regression problems.

Plot prediction intervals

Plot prediction intervals

Data with gamma distribution

Data with gamma distribution

Use a pre-trained model

Use a pre-trained model

Data with uneven uncertainty

Data with uneven uncertainty

Data with constant uncertainty

Data with constant uncertainty

Tutorial for time series

Tutorial for time series

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2. Advanced analysis

The following examples use MAPIE for discussing more complex MAPIE problems.

Conformal Predictive Distribution

Conformal Predictive Distribution

The symmetric correction parameter in conformalized quantile regression

The symmetric correction parameter in conformalized quantile regression

Hyperparameters tuning with cross-conformal regression

Hyperparameters tuning with cross-conformal regression

Estimating aleatoric and epistemic uncertainties

Estimating aleatoric and epistemic uncertainties

EnbPI technique for time series

EnbPI technique for time series

Focus on intervals width

Focus on intervals width

Focus on residual normalised score

Focus on residual normalised score

Focus on local (or “conditional”) coverage

Focus on local (or "conditional") coverage

Conformalized quantile regression on gamma distributed data

Conformalized quantile regression on gamma distributed data

Coverage validity for regression tasks

Coverage validity for regression tasks

sphx_glr_examples_regression_2-advanced-analysis_plot_main-tutorial-regression.py

# Comparison between conformalized quantile regressor and cross methods

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3. Simulations from scientific articles

The following examples reproduce the simulations from the scientific articles that introduce the methods implemented in MAPIE for regression settings.

Predictive inference with the jackknife+, Foygel-Barber et al. (2020)

Predictive inference with the jackknife+, Foygel-Barber et al. (2020)

Adaptive conformal predictions for time series, Zaffran et al. (2022)

Adaptive conformal predictions for time series, Zaffran et al. (2022)

Predictive inference is free with the Jackknife+-after-Bootstrap, Kim et al. (2020)

Predictive inference is free with the Jackknife+-after-Bootstrap, Kim et al. (2020)

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4. Other notebooks

This section lists a series of Jupyter notebooks hosted on the MAPIE Github repository that can be run on Google Colab:

• Estimating prediction intervals for time series forecast with EnbPI and ACI