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DOC: Put all regression notebooks in the example section (and remove the notebooks_regression one). Mark them as pre-v1.
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‎doc/index_regression.rst

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:maxdepth: 2
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choosing_the_right_algorithm_regression
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examples_regression/4-tutorials/plot_main-tutorial-regression
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examples_regression/4-tutorials/plot_cqr_tutorial
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examples_regression/4-tutorials/plot_ts-tutorial
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examples_regression/1-quickstart/plot_toy_model
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examples_regression/index
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notebooks_regression
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theoretical_description_regression
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theoretical_description_conformity_scores

‎doc/notebooks_regression.rst

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‎examples/regression/1-quickstart/plot_compare_conformity_scores.py

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"""
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===========================================================
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Estimating prediction intervals of Gamma distributed target
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===========================================================
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==========================================================================================
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[Pre-v1] Estimating prediction intervals of Gamma distributed target
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==========================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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This example uses :class:`~mapie.regression.MapieRegressor` to estimate
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prediction intervals associated with Gamma distributed target.
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The limit of the absolute residual conformity score is illustrated.

‎examples/regression/1-quickstart/plot_cqr_symmetry_difference.py

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"""
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====================================
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Plotting CQR with symmetric argument
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====================================
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===================================================================
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[Pre-v1] Plotting CQR with symmetric argument
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===================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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An example plot of :class:`~mapie.quantile_regression.MapieQuantileRegressor`
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illustrating the impact of the symmetry parameter.
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"""

‎examples/regression/1-quickstart/plot_heteroscedastic_1d_data.py

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"""
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============================================================
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Estimate the prediction intervals of 1D heteroscedastic data
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============================================================
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===========================================================================================
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[Pre-v1] Estimate the prediction intervals of 1D heteroscedastic data
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===========================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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:class:`~mapie.regression.MapieRegressor` and
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:class:`~mapie.quantile_regression.MapieQuantileRegressor` is used

‎examples/regression/1-quickstart/plot_homoscedastic_1d_data.py

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"""
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==========================================================
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Estimate the prediction intervals of 1D homoscedastic data
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==========================================================
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=========================================================================================
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[Pre-v1] Estimate the prediction intervals of 1D homoscedastic data
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=========================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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:class:`~mapie.regression.MapieRegressor` and
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:class:`~mapie.quantile_regression.MapieQuantileRegressor`

‎examples/regression/1-quickstart/plot_prefit.py

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"""
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===========================================================================
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Example use of the prefit parameter with neural networks and LGBM Regressor
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===========================================================================
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==========================================================================================================
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[Pre-v1] Example use of the prefit parameter with neural networks and LGBM Regressor
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==========================================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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:class:`~mapie.regression.MapieRegressor` and
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:class:`~mapie.quantile_regression.MapieQuantileRegressor`

‎examples/regression/1-quickstart/plot_toy_model.py

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"""
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======================================================
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Plotting MAPIE prediction intervals with a toy dataset
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======================================================
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=====================================================================================
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[Pre-v1] Plotting MAPIE prediction intervals with a toy dataset
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=====================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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An example plot of :class:`~mapie.regression.MapieRegressor` used
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in the Quickstart.
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"""

‎examples/regression/2-advanced-analysis/README.rst

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.. _regression_examples_2:
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-----
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2. Advanced analysis
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--------------------
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‎examples/regression/2-advanced-analysis/plot-coverage-width-based-criterion.py

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"""
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================================================
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Estimating coverage width based criterion
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================================================
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===============================================================================
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[Pre-v1] Estimating coverage width based criterion
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===============================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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This example uses :class:`~mapie.regression.MapieRegressor`,
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:class:`~mapie.quantile_regression.MapieQuantileRegressor` and
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:class:`~mapie.metrics` is used to estimate the coverage width

‎examples/regression/2-advanced-analysis/plot_both_uncertainties.py

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"""
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================================================
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Estimating aleatoric and epistemic uncertainties
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================================================
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===============================================================================
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[Pre-v1] Estimating aleatoric and epistemic uncertainties
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===============================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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This example uses :class:`~mapie.regression.MapieRegressor` and
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:class:`~mapie.quantile_regression.MapieQuantileRegressor` to estimate
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prediction intervals capturing both aleatoric and epistemic uncertainties

‎examples/regression/2-advanced-analysis/plot_conditional_coverage.py

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"""
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===============================
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Estimating conditional coverage
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===============================
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==============================================================
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[Pre-v1] Estimating conditional coverage
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==============================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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This example uses :func:`~mapie.regression.MapieRegressor` with conformal
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scores that returns adaptive intervals i.e.
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(:class:`~mapie.conformity_scores.GammaConformityScore` and

‎examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py

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"""
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=================================================
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Make Conformal Predictive Distribution with MAPIE
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=================================================
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================================================================================
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[Pre-v1] Make Conformal Predictive Distribution with MAPIE
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================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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"""
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##############################################################################

‎examples/regression/2-advanced-analysis/plot_coverage_validity.py

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"""
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Coverage Validity with MAPIE for Regression Task
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===============================================================================
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[Pre-v1] Coverage Validity with MAPIE for Regression Task
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===============================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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This example verifies that conformal claims are valid in the MAPIE package
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when using the CP prefit/split methods.

‎examples/regression/2-advanced-analysis/plot_nested-cv.py

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"""
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Nested cross-validation for estimating prediction intervals
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==========================================================================================
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[Pre-v1] Nested cross-validation for estimating prediction intervals
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==========================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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This example compares non-nested and nested cross-validation strategies for
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estimating prediction intervals with :class:`~mapie.regression.MapieRegressor`.

‎examples/regression/3-scientific-articles/README.rst

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.. _regression_examples_3:
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-----
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3. Simulations from scientific articles
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---------------------------------------
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‎examples/regression/3-scientific-articles/plot_barber2020_simulations.py

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"""
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Reproducing the simulations from Foygel-Barber et al. (2020)
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===========================================================================================
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[Pre-v1] Reproducing the simulations from Foygel-Barber et al. (2020)
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===========================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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:class:`~mapie.regression.MapieRegressor` is used to investigate
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the coverage level and the prediction interval width as a function

‎examples/regression/3-scientific-articles/plot_kim2020_simulations.py

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"""
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==================================================
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Reproducing the simulations from Kim et al. (2020)
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==================================================
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=================================================================================
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[Pre-v1] Reproducing the simulations from Kim et al. (2020)
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=================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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:class:`~mapie.regression.MapieRegressor` is used to reproduce the simulations
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by Kim et al. (2020) [1] in their article which introduces the

‎examples/regression/3-scientific-articles/plot_zaffran2022_comparison.py

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"""
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Reproduction of part of the paper experiments of Zaffran et al. (2022)
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[Pre-v1] Reproduction of part of the paper experiments of Zaffran et al. (2022)
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=====================================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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:class:`~mapie.regression.MapieTimeSeriesRegressor` is used to reproduce a
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part of the paper experiments of Zaffran et al. (2022) in their article [1]

‎examples/regression/4-tutorials/README.rst

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.. _regression_examples_4:
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-----
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4. Tutorials
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------------
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‎examples/regression/4-tutorials/plot_ResidualNormalisedScore_tutorial.py

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"""
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Tutorial for residual normalised score
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=====================================================================
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[Pre-v1] Tutorial for residual normalised score
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=====================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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We will use the sklearn california housing dataset to understand how the
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residual normalised score works and show the multiple ways of using it.
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‎examples/regression/4-tutorials/plot_cqr_tutorial.py

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"""
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Tutorial for conformalized quantile regression (CQR)
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[Pre-v1] Tutorial for conformalized quantile regression (CQR)
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===================================================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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We will use the sklearn california housing dataset as the base for the
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comparison of the different methods available on MAPIE. Two classes will

‎examples/regression/4-tutorials/plot_main-tutorial-regression.py

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r"""
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Tutorial for tabular regression
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[Pre-v1] Tutorial for tabular regression
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==============================================================
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes.**
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**This notebook hasn't been updated to the new API yet.**
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In this tutorial, we compare the prediction intervals estimated by MAPIE on a
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simple, one-dimensional, ground truth function ``f(x) = x * sin(x)``.

‎examples/regression/5-other-notebooks/README.rst

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.. _regression_examples_5:
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-----
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5. Other notebooks
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--------------------------------------------
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This section lists a series of Jupyter notebooks hosted on the MAPIE Github repository that can be run on Google Colab:
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- `[Pre-v1] Estimating the uncertainties of homoscedastic and heteroscedastic noisy data for tabular regression <https://github.com/scikit-learn-contrib/MAPIE/blob/master/notebooks/regression/tutorial_regression.ipynb>`_
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- `[Pre-v1] Estimating the uncertainties in the exoplanet masses <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/exoplanets.ipynb>`_
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- `[Pre-v1] Estimating prediction intervals for time series forecast with EnbPI and ACI <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/ts-changepoint.ipynb>`_
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‎examples/regression/README.rst

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.. _regression_examples:
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Regression examples
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===================
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===================
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Following is a collection of notebooks demonstrating how to use MAPIE.
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**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes. Not all notebooks have been updated to the new API yet. These are marked as `[Pre-v1]`.**

‎notebooks/regression/exoplanets.ipynb

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"cell_type": "markdown",
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"id": "88266fe7-93bb-48c7-a84b-7a3ebe24c2c3",
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"metadata": {},
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"source": [
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"# Estimating the uncertainties in the exoplanet masses"
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]
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"source": "# Estimating the uncertainties in the exoplanet masses"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes. This notebook hasn't been updated to the new API yet.**",
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"id": "43970544651bd3bb"
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},
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{
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"cell_type": "markdown",
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"id": "66082f1a-7415-4392-973f-a267b23ec64b",
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"metadata": {},
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"source": [
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"[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/regression/exoplanets.ipynb)\n"
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]
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"source": "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/regression/exoplanets.ipynb)\n"
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},
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{
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"cell_type": "markdown",

‎notebooks/regression/tutorial_regression.ipynb

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"cell_type": "markdown",
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"id": "a1d2af1c",
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"metadata": {},
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"source": [
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"# Tutorial for regression"
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]
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"source": "# Tutorial for regression"
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": "**Note: we recently released MAPIE v1.0.0, which introduces breaking API changes. This notebook hasn't been updated to the new API yet.**",
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"id": "1a0c1682ad61ba3e"
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},
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{
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"cell_type": "markdown",
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"id": "9a2e808e",
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"metadata": {},
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"source": [
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"In this tutorial, we compare the prediction intervals estimated by MAPIE on a \n",
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"In this tutorial, we compare the prediction intervals estimated by MAPIE on a\n",
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"simple, one-dimensional, ground truth function\n",
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"\n",
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"$$\n",

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