EarlyStopping

Early stopping is a Python library implementing computationally efficient model selection methods for iterative estimation procedures based on the theory in:

• Early stopping for statistical inverse problems via truncated SVD estimation.

  • 7. Blanchard, M. Hoffmann, M. Reiß. In Electronic Journal of Statistics 12(2): 3204-3231 (2018).

• Optimal adaptation for early stopping in statistical inverse problems.

  • 7. Blanchard, M. Hoffmann, M. Reiß. In SIAM/ASA Journal of Uncertainty Quantification 6(3), 1043–1075 (2018).

• Early stopping for L2-boosting in high-dimensional linear models.

  • 2. Stankewitz. arXiv:2210.07850 [math.ST] (2022).

• Estimation and inference of treatment effects with L2-boosting in high-dimensional settings.

  • 10. Kueck, Y. Luo, M. Spindler, Z. Wang. In Journal of Econometrics 234(2), 714-731 (2023).

• Early stopping for conjugate gradients in statistical inverse problems.

  • 12. Hucker, M. Reiß. arXiv:2406.15001 [math.ST] (2024).

Contents

• Installation
• Documentation
• Examples

Indices and tables

• Index

• Module Index

• Search Page