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1. Full Text The “double cross-validation” software tool for MLR QSAR model development
by Roy, Kunal and Ambure, Pravin
Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, 12/2016, Volume 159, pp. 108 - 126
Quantitative structure–activity relationship (QSAR) modeling is a widely used computational technique applied in various fields including rational drug design,... 
Validation set | Double cross-validation | QSPR | QSAR | Prediction errors
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2. Full Text Local warming and violent armed conflict in Africa
by van Weezel, Stijn
World Development, ISSN 0305-750X, 02/2020, Volume 126, p. 104708
Research on the effect of climate change on violent armed conflict relies almost exclusively on analysing annual variation in climatic conditions. A... 
Civil conflict | Climate change | Bayesian model averaging | Cross-validation | Africa
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3. Full Text Principal component analysis
by Bro, R and Smilde, A.K
Analytical Methods, ISSN 1759-9660, 2014, Volume 6, Issue 9, pp. 2812 - 2831
Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a... 
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4. Full Text Principal component analysis
by Bro, Rasmus and Smilde, Age K
Analytical Methods, ISSN 1759-9660, 05/2014, Volume 6, Issue 9, pp. 2812 - 2831
Principal component analysis is one of the most important and powerful methods in chemometrics as well as in a wealth of other areas. This paper provides a... 
CHEMISTRY, ANALYTICAL | NUMBER | ROBUSTNESS | FOOD SCIENCE & TECHNOLOGY | CLASSIFICATION | MULTIVARIATE CALIBRATION | PCA | LINEAR TREND | EIGENVALUES | BOOTSTRAP | SPECTROSCOPY | MATRICES | CROSS-VALIDATION
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5. Full Text Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory
by Watanabe, Sumio
Journal of Machine Learning Research, ISSN 1532-4435, 12/2010, Volume 11, pp. 3571 - 3594
In regular statistical models, the leave-one-out cross-validation is asymptotically equivalent to the Akaike information criterion. However, since many... 
Singular learning machine | Birational invariant | Information criterion | Cross-validation | REGRESSION | MACHINES | birational invariant | MODEL SELECTION | cross-validation | information criterion | NETWORKS | STOCHASTIC COMPLEXITY | AUTOMATION & CONTROL SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | singular learning machine
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6. Full Text Turning straw into gold: building robustness into gene signature inference
by Goh, Wilson Wen Bin and Wong, Limsoon
Drug Discovery Today, ISSN 1359-6446, 01/2019, Volume 24, Issue 1, pp. 31 - 36
Reproducible and generalizable gene signatures are essential for clinical deployment, but are hard to come by. The primary issue is insufficient mitigation of... 
CROSS-VALIDATION | PHARMACOLOGY & PHARMACY | REPRODUCIBILITY | Genes | Analysis
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7. Full Text Estimating the subgroup and testing for treatment effect in a post-hoc analysis of a clinical trial with a biomarker
by Joshi, Neha and Fine, Jason and Chu, Rong and Ivanova, Anastasia
Journal of Biopharmaceutical Statistics, ISSN 1054-3406, 07/2019, Volume 29, Issue 4, pp. 685 - 695
We consider the problem of estimating a biomarker-based subgroup and testing for treatment effect in the overall population and in the subgroup after the... 
biomarker | Subgroup | cross-validation
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8. Full Text A collaborative filtering approach to mitigate the new user cold start problem
by Bobadilla, Jesús and Ortega, Fernando and Hernando, Antonio and Bernal, Jesús
Knowledge-Based Systems, ISSN 0950-7051, 2012, Volume 26, pp. 225 - 238
The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system... 
Neural learning | Collaborative filtering | Similarity measures | Leave-one-out-cross validation | Cold start | Recommender systems | RECOMMENDER | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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9. Full Text Making better Maxent models of species distributions: complexity, overfitting and evaluation
by Radosavljevic, Aleksandar and Anderson, Robert P and Araújo, Miguel
Journal of Biogeography, ISSN 0305-0270, 04/2014, Volume 41, Issue 4, pp. 629 - 643
Aim Models of species niches and distributions have become invaluable to biogeographers over the past decade, yet several outstanding methodological issues... 
evaluation | Heteromys | South America | overfitting | Maxent | tuning | rodent | Cross validation | niche | smoothing | Evaluation | Smoothing | Niche | Rodent | Tuning | Overfitting | NICHE MODELS | PERFORMANCE | TRANSFERABILITY | VALIDATION | CLIMATE-CHANGE | GEOGRAPHY, PHYSICAL | SAMPLE SELECTION BIAS | INVASIONS | ECOLOGY | IMPACTS | ENVELOPE MODELS | RANGE | Discrimination in insurance | Analysis | Models
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10. Full Text Comments on the Definition of the Q(2) Parameter for QSAR Validation
by Consonni, V and Ballabio, D and Todeschini, R
JOURNAL OF CHEMICAL INFORMATION AND MODELING, ISSN 1549-9596, 07/2009, Volume 49, Issue 7, pp. 1669 - 1678
This paper deals with the problem of evaluating the predictive ability of QSAR models and continues the discussion about proper estimates of the predictive... 
COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | TEST SETS | MODELS | CROSS-VALIDATION | COMPUTER SCIENCE, INFORMATION SYSTEMS | EXTERNAL VALIDATION | SELECTION | CHEMISTRY, MULTIDISCIPLINARY | PREDICTION
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11. Full Text Efficient estimates in regression models with highly correlated covariates
by Koukouvinos, Christos and Mitrouli, Marilena and Turek, Ondřej
Journal of Computational and Applied Mathematics, ISSN 0377-0427, 08/2019, p. 112416
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12. Full Text A note on the validity of cross-validation for evaluating autoregressive time series prediction
by Bergmeir, Christoph and Hyndman, Rob J and Koo, Bonsoo
Computational Statistics and Data Analysis, ISSN 0167-9473, 04/2018, Volume 120, pp. 70 - 83
One of the most widely used standard procedures for model evaluation in classification and regression is -fold cross-validation (CV). However, when it comes to... 
Autoregression | Cross-validation | Time series | STATISTICS & PROBABILITY | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | Usage | Analysis | Machine learning
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13. Full Text Classification of membrane protein types using Voting Feature Interval in combination with Chou׳s Pseudo Amino Acid Composition
by Ali, Farman and Hayat, Maqsood
Journal of Theoretical Biology, ISSN 0022-5193, 11/2015, Volume 384, pp. 78 - 83
Membrane protein is a major constituent of cell, performing numerous crucial functions in the cell. These functions are mostly concerned with membrane... 
VFI | SVM | 10-fold cross validation | Membrane proteins
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14. Full Text Overfitting in making comparisons between variable selection methods
by Reunanen, Juha
Journal of Machine Learning Research, ISSN 1532-4435, 03/2003, Volume 3, pp. 1371 - 1382
Algorithm comparison | Cross-validation | Knearest neighbors | Overfitting | Variable selection
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15. Full Text Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
by Wong, Tzu-Tsung
Pattern Recognition, ISSN 0031-3203, 09/2015, Volume 48, Issue 9, pp. 2839 - 2846
Classification is an essential task for predicting the class values of new instances. Both -fold and leave-one-out cross validation are very popular for... 
k-Fold cross validation | Classification | Independence | Leave-one-out cross validation | Sampling distribution | VARIANCE | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | ENGINEERING, ELECTRICAL & ELECTRONIC | Analysis | Algorithms
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16. Full Text Model selection and assessment for multi-species occupancy models
by Broms, Kristin M and Hooten, Mevin B and Fitzpatrick, Ryan M
Ecology, ISSN 0012-9658, 07/2016, Volume 97, Issue 7, pp. 1759 - 1770
South platte river basin | Plains fish | Species distribution maps | Cross-validation | Bayesian hierarchical models | Biodiversity
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17. Full Text Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC
by Vehtari, Aki and Gelman, Andrew and Gabry, Jonah
Statistics and Computing, ISSN 0960-3174, 08/2016, Volume 27, Issue 5, pp. 1413 - 1432
Leave-one-out cross-validation (LOO) and the widely applicable information criterion (WAIC) are methods for estimating pointwise out-of-sample prediction... 
Stan | Widely applicable information criterion (WAIC) | Bayesian computation | Pareto smoothed importance sampling (PSIS) | Leave-one-out cross-validation (LOO) | K-fold cross-validation | STATISTICS & PROBABILITY | COMPUTER SCIENCE, THEORY & METHODS | Computer science | Analysis | Models
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18. Full Text Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
by Yarkoni, Tal and Westfall, Jacob
Perspectives on Psychological Science, ISSN 1745-6916, 11/2017, Volume 12, Issue 6, pp. 1100 - 1122
Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly... 
prediction | explanation | machine learning | PERSONALITY | BIAS | POLYMORPHISM | LINEAR-MODELS | PSYCHOLOGY, MULTIDISCIPLINARY | STATISTICAL POWER | MODEL SELECTION | CROSS-VALIDATION | CONSEQUENCES | ARCHITECTURE | REGRESSION SHRINKAGE | Psychology - methods | Machine Learning | Humans | Learning | Accuracy | Behavior | Artificial intelligence | Psychology
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19. Full Text Averaging estimators for discrete choice by [Formula presented]-fold cross-validation
by Zhao, Shangwei and Zhou, Jianhong and Yang, Guangren
Economics Letters, ISSN 0165-1765, 01/2019, Volume 174, pp. 65 - 69
Considering model averaging estimation under multinomial and ordered logit models, we propose a weight choice method by minimizing an [Formula presented]-fold... 
Asymptotically optimality | Cross-validation | Model averaging | Computational cost
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20. Full Text An adaptive inverse-distance weighting spatial interpolation technique
by Lu, George Y and Wong, David W
Computers and Geosciences, ISSN 0098-3004, 2008, Volume 34, Issue 9, pp. 1044 - 1055
One of the most frequently used deterministic models in spatial interpolation is the inverse-distance weighting (IDW) method. It is relatively fast and easy to... 
Spatial interpolation | Cross validation | Distance-decay parameter | Adaptive inverse-distance weighting | Kriging | DISTRIBUTIONS | COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS | GEOSCIENCES, MULTIDISCIPLINARY | cross validation | adaptive inverse-distance weighting | kriging | spatial interpolation | ELEVATION | distance-decay parameter | PREDICTION
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