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Evaluation of Bagging Predictors with Kernel Density Estimation and Bagging Score

arXiv:2604.03599v1 Announce Type: new Abstract: For a larger set of predictions of several differently trained machine learning models, known as bagging predictors, the mean of all predictions is taken by default. Nevertheless, this proceeding can deviate from the actual ground truth in certain parameter regions. An approach is presented to determine a representative y_BS from such a set of predictions using Kernel Density Estimation (KDE) in nonlinear regression with Neural Networks (NN) which simultaneously provides an associated quality criterion beta_BS, called Bagging Score (BS), that reflects the confidence of the obtained ensemble prediction. It is shown that working with the new approach better predictions can be made than working with the common use of mean or median. In addition to this, the used method is contrasted to several approaches of nonlinear regression from the literatur, resulting in a top ranking in each of the calculated error values without using any optimizati

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Philipp Seitz, Jan Schmitt, Andreas Schiffler · · 1 min read · 14 views
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arXiv:2604.03599v1 Announce Type: new Abstract: For a larger set of predictions of several differently trained machine learning models, known as bagging predictors, the mean of all predictions is taken by default. Nevertheless, this proceeding can deviate from the actual ground truth in certain parameter regions. An approach is presented to determine a representative y_BS from such a set of predictions using Kernel Density Estimation (KDE) in nonlinear regression with Neural Networks (NN) which simultaneously provides an associated quality criterion beta_BS, called Bagging Score (BS), that reflects the confidence of the obtained ensemble prediction. It is shown that working with the new approach better predictions can be made than working with the common use of mean or median. In addition to this, the used method is contrasted to several approaches of nonlinear regression from the literatur, resulting in a top ranking in each of the calculated error values without using any optimization or feature selection technique.

Executive Summary

This article proposes an innovative approach to evaluating bagging predictors, a set of predictions from differently trained machine learning models. The authors introduce Kernel Density Estimation (KDE) to determine a representative prediction and an associated quality criterion, called Bagging Score (BS). The new approach is shown to outperform the common use of mean or median predictions, and it ranks top in error values compared to several nonlinear regression methods without using optimization or feature selection techniques. The article contributes to the field of machine learning and has significant implications for both practical applications and policy-making.

Key Points

  • ▸ Kernel Density Estimation (KDE) is used to determine a representative prediction from a set of bagging predictors.
  • ▸ The Bagging Score (BS) is introduced as a quality criterion to reflect the confidence of the ensemble prediction.
  • ▸ The new approach outperforms mean and median predictions and ranks top in error values compared to other nonlinear regression methods.

Merits

Improved Accuracy

The proposed approach shows better predictions than the common use of mean or median predictions, indicating improved accuracy in bagging predictors.

Robustness to Nonlinearity

The use of KDE and BS allows the approach to handle nonlinear relationships between variables, making it more robust to complex data.

Demerits

Computational Complexity

The introduction of KDE and BS may increase computational complexity, which could be a limitation in large-scale applications.

Interpretability

The use of KDE and BS may reduce interpretability of the results, as the Bagging Score is a complex quality criterion.

Expert Commentary

This article presents a novel approach to evaluating bagging predictors, which leverages the power of Kernel Density Estimation and the Bagging Score. The results are promising, with improved accuracy and robustness to nonlinearity. However, the introduction of KDE and BS may increase computational complexity and reduce interpretability. The article's implications for both practical applications and policy-making are significant, making it a valuable contribution to the field of machine learning. Future research should focus on addressing the limitations of the proposed approach and exploring its applications in real-world scenarios.

Recommendations

  • ✓ Future research should investigate the application of the proposed approach to other machine learning tasks and domains.
  • ✓ The article's authors should explore ways to reduce computational complexity and increase interpretability of the results.

Sources

Original: arXiv - cs.LG

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