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The difference of model robustness assessment using cross‐validation and bootstrap methods
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/cem.1290openalex-snapshot
Evaluation of model predictive ability by external validation techniques
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QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo‐)triazoles
inferredcites →https://doi.org/10.1002/jcc.23576openalex-snapshot
QSARINS‐chem: Insubria datasets and new QSAR/QSPR models for environmental pollutants in QSARINS
inferredcites →https://doi.org/10.1002/minf.202200072openalex-snapshot
The Relevance of Goodness‐of‐fit, Robustness and Prediction Validation Categories of OECD‐QSAR Principles with Respect to Sample Size and Model Type
inferredcites →https://doi.org/10.1002/qsar.200610151openalex-snapshot
Principles of QSAR models validation: internal and external
inferredcites →https://doi.org/10.1007/978-0-387-84858-7openalex-snapshot
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
inferredcites →https://doi.org/10.1007/s00216-007-1790-1openalex-snapshot
Cross-validation of component models: A critical look at current methods
inferredcites →https://doi.org/10.1016/j.chemolab.2020.104106openalex-snapshot
Effect of variable allocation on validation and optimality parameters and on cross-optimization perspectives
inferredcites →https://doi.org/10.1016/j.jhazmat.2009.01.002openalex-snapshot
Prediction of upper flammability limit percent of pure compounds from their molecular structures
inferredcites →https://doi.org/10.1016/j.jmgm.2006.06.005openalex-snapshot
Statistical external validation and consensus modeling: A QSPR case study for Koc prediction
inferredcites →https://doi.org/10.1016/j.scitotenv.2013.10.068openalex-snapshot
Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis
inferredcites →https://doi.org/10.1016/s0165-9936(03)00607-1openalex-snapshot
Cross-validation as the objective function for variable-selection techniques
inferredcites →https://doi.org/10.1016/s0169-7439(00)00102-7openalex-snapshot
The bootstrap: a tutorial
inferredcites →https://doi.org/10.1021/ci050212lopenalex-snapshot
Statistically Validated QSARs, Based on Theoretical Descriptors, for Modeling Aquatic Toxicity of Organic Chemicals in Pimephales promelas (Fathead Minnow)
inferredcites →https://doi.org/10.1021/ci800253uopenalex-snapshot
External Validation and Prediction Employing the Predictive Squared Correlation Coefficient — Test Set Activity Mean vs Training Set Activity Mean
inferredcites →https://doi.org/10.1021/ci900115yopenalex-snapshot
Comments on the Definition of the Q 2 Parameter for QSAR Validation
inferredcites →https://doi.org/10.1039/c7ay01363aopenalex-snapshot
Common mistakes in cross-validating classification models
inferredcites →https://doi.org/10.1080/10629360802550697openalex-snapshot
Externally validated QSPR modelling of VOC tropospheric oxidation by NO3 radicals
inferredcites →https://doi.org/10.1080/1062936x.2015.1084647openalex-snapshot
Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters
inferredcites →https://doi.org/10.1080/1062936x.2016.1196388openalex-snapshot
Robust modelling of acute toxicity towards fathead minnow (Pimephales promelas)using counter-propagation artificial neural networks and genetic algorithm
inferredcites →https://doi.org/10.1080/1062936x.2021.1890208openalex-snapshot
Sample-size dependence of validation parameters in linear regression models and in QSAR
inferredcites →https://doi.org/10.1080/1062936x.2023.2214871openalex-snapshot
Selection of optimal validation methods for quantitative structure–activity relationships and applicability domain
inferredcites →https://doi.org/10.1186/s13321-015-0082-6openalex-snapshot
QSAR DataBank repository: open and linked qualitative and quantitative structure–activity relationship models
inferredcites →https://doi.org/10.1186/s13321-023-00757-1openalex-snapshot
Patch seriation to visualize data and model parameters
inferredcites →https://doi.org/10.17632/2mmwv3j4msopenalex-snapshot
Daily air pollution and meteorological data Budapest, 2007
inferredcites →https://doi.org/10.3390/molecules26041111openalex-snapshot
Effect of Dataset Size and Train/Test Split Ratios in QSAR/QSPR Multiclass Classification
inferredcites →https://doi.org/10.4018/ijqspr.20200701.oa1openalex-snapshot
Principles of QSAR Modeling
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5555/1756006.1859921openalex-snapshot
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Origin of the OECD Principles for QSAR Validation and Their Role in Changing the QSAR Paradigm Worldwide: An Historical Overview
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Comparative Machine Learning Approaches for Battery Capacity Prediction Using Random Forest and LSBoost Regression Algorithms
inferred← citeshttps://doi.org/10.2139/ssrn.6845518openalex-snapshot
QSAR models on medium size datasets: Bias-variance tradeoff or double descent? Bagging or resampling without replacement?
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