Is K-fold cross validation the best model selection method for Machine Learning?

• URL: http://arxiv.org/abs/2401.16407v2
• Date: Fri, 08 Nov 2024 07:30:25 GMT
• Title: Is K-fold cross validation the best model selection method for Machine Learning?
• Authors: Juan M Gorriz, R. Martin Clemente, F Segovia, J Ramirez, A Ortiz, J. Suckling,
• Abstract summary: K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is generated by chance. A novel statistical test based on K-fold CV and the Upper Bound of the actual risk (K-fold CUBV) is proposed.
• Score: 0.0
• License:
• Abstract: As a technique that can compactly represent complex patterns, machine learning has significant potential for predictive inference. K-fold cross-validation (CV) is the most common approach to ascertaining the likelihood that a machine learning outcome is generated by chance, and it frequently outperforms conventional hypothesis testing. This improvement uses measures directly obtained from machine learning classifications, such as accuracy, that do not have a parametric description. To approach a frequentist analysis within machine learning pipelines, a permutation test or simple statistics from data partitions (i.e., folds) can be added to estimate confidence intervals. Unfortunately, neither parametric nor non-parametric tests solve the inherent problems of partitioning small sample-size datasets and learning from heterogeneous data sources. The fact that machine learning strongly depends on the learning parameters and the distribution of data across folds recapitulates familiar difficulties around excess false positives and replication. A novel statistical test based on K-fold CV and the Upper Bound of the actual risk (K-fold CUBV) is proposed, where uncertain predictions of machine learning with CV are bounded by the worst case through the evaluation of concentration inequalities. Probably Approximately Correct-Bayesian upper bounds for linear classifiers in combination with K-fold CV are derived and used to estimate the actual risk. The performance with simulated and neuroimaging datasets suggests that K-fold CUBV is a robust criterion for detecting effects and validating accuracy values obtained from machine learning and classical CV schemes, while avoiding excess false positives.

Related papers

• Risk and cross validation in ridge regression with correlated samples [72.59731158970894]
  We provide training examples for the in- and out-of-sample risks of ridge regression when the data points have arbitrary correlations. We further extend our analysis to the case where the test point has non-trivial correlations with the training set, setting often encountered in time series forecasting. We validate our theory across a variety of high dimensional data.
  arXiv  Detail & Related papers  (2024-08-08T17:27:29Z)
• Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data [7.62566998854384]
  Cross-validation is used for several tasks such as estimating the prediction error, tuning the regularization parameter, and selecting the most suitable predictive model. The K-fold cross-validation is a popular CV method but its limitation is that the risk estimates are highly dependent on the partitioning of the data. This study presents an alternative novel predictive performance test and valid confidence intervals based on exhaustive nested cross-validation.
  arXiv  Detail & Related papers  (2024-08-06T12:28:16Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Theoretical characterization of uncertainty in high-dimensional linear classification [24.073221004661427]
  We show that uncertainty for learning from limited number of samples of high-dimensional input data and labels can be obtained by the approximate message passing algorithm. We discuss how over-confidence can be mitigated by appropriately regularising, and show that cross-validating with respect to the loss leads to better calibration than with the 0/1 error.
  arXiv  Detail & Related papers  (2022-02-07T15:32:07Z)
• Self-Certifying Classification by Linearized Deep Assignment [65.0100925582087]
  We propose a novel class of deep predictors for classifying metric data on graphs within PAC-Bayes risk certification paradigm. Building on the recent PAC-Bayes literature and data-dependent priors, this approach enables learning posterior distributions on the hypothesis space.
  arXiv  Detail & Related papers  (2022-01-26T19:59:14Z)
• Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning [78.83598532168256]
  Marginal-likelihood based model-selection is rarely used in deep learning due to estimation difficulties. Our work shows that marginal likelihoods can improve generalization and be useful when validation data is unavailable.
  arXiv  Detail & Related papers  (2021-04-11T09:50:24Z)
• Deep Learning in current Neuroimaging: a multivariate approach with power and type I error control but arguable generalization ability [0.158310730488265]
  A non-parametric framework is proposed that estimates the statistical significance of classifications using deep learning architectures. A label permutation test is proposed in both studies using cross-validation (CV) and resubstitution with upper bound correction (RUB) as validation methods. We found in the permutation test that CV and RUB methods offer a false positive rate close to the significance level and an acceptable statistical power.
  arXiv  Detail & Related papers  (2021-03-30T21:15:39Z)
• Good Classifiers are Abundant in the Interpolating Regime [64.72044662855612]
  We develop a methodology to compute precisely the full distribution of test errors among interpolating classifiers. We find that test errors tend to concentrate around a small typical value $varepsilon*$, which deviates substantially from the test error of worst-case interpolating model. Our results show that the usual style of analysis in statistical learning theory may not be fine-grained enough to capture the good generalization performance observed in practice.
  arXiv  Detail & Related papers  (2020-06-22T21:12:31Z)
• Estimating the Prediction Performance of Spatial Models via Spatial k-Fold Cross Validation [1.7205106391379026]
  In machine learning one often assumes the data are independent when evaluating model performance. spatial autocorrelation (SAC) causes the standard cross validation (CV) methods to produce optimistically biased prediction performance estimates. We propose a modified version of the CV method called spatial k-fold cross validation (SKCV) which provides a useful estimate for model prediction performance without optimistic bias due to SAC.
  arXiv  Detail & Related papers  (2020-05-28T19:55:18Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.