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The use of machine learning (ML) methods for prediction and forecasting has become widespread across the quantitative sciences.
A checklist is associated with increased quality of reporting preclinical biomedical research: A systematic review
Han, S., Olonisakin, T. F., Pribis, J. P., Zupetic, J., Yoon, J. H., Holleran, K. M., Jeong, K., Shaikh, N., Rubio, D. M., and Lee, J. S · 1932
Earlier work this paper cites.
Reproducibility in machine learning for health research: Still a ways to go
McDermott, M. B. A., Wang, S., Marinsek, N., Ranganath, R., Foschini, L., and Ghassemi, M · 1946
Earlier work this paper cites.
Improving Supreme Court Forecasting Using Boosted Decision Trees
Kaufman, A. R., Kraft, P., and Sen, M · 1987
Earlier work this paper cites.
Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data
Muchlinski, D., Siroky, D., He, J., and Kocher, M · 1987
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Seeing the Forest through the Trees
Muchlinski, D. A., Siroky, D., He, J., and Kocher, M. A · 1987
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How Cross-Validation Can Go Wrong and What to Do About It
Neunhoeffer, M. and Sternberg, S · 1987
Earlier work this paper cites.
Comparing Random Forest with Logistic Regression for Predicting Class-Imbalanced Civil War Onset Data: A Comment
Wang, Y · 1987
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Approximate statistical tests for comparing supervised classification learning algorithms
Dietterich, T. G · 1998
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Multiple imputation: a primer
Schafer, J. L · 1999
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On the Incidence of Civil War in Africa
Collier, P. and Hoeffler, A · 2002
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Ethnicity, Insurgency, and Civil War
Fearon, J. D. and Laitin, D. D · 2003
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Pineau, J., Vincent-Lamarre, P., Sinha, K., Larivière, V., Beygelzimer, A., d’Alché Buc, F., Fox, E., and Larochelle, H · 2003
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Review: A gentle introduction to imputation of missing values
Donders, A. R. T., van der Heijden, G. J. M. G., Stijnen, T., and Moons, K. G. M · 2006
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An introduction to ROC analysis
Fawcett, T · 2006
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Sensitivity Analysis of Empirical Results on Civil War Onset:
Hegre, H. and Sambanis, N · 2006
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Handbook of Statistical Analysis and Data Mining Applications
Nisbet, R., Elder, J., and Miner, G · 2009
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pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., and Müller, M · 2011
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Reporting and Methods in Clinical Prediction Research: A Systematic Review
Bouwmeester, W., Zuithoff, N. P. A., Mallett, S., Geerlings, M. I., Vergouwe, Y., Steyerberg, E. W., Altman, D. G., and Moons, K. G. M · 2012
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Leakage in data mining: Formulation, detection, and avoidance
Kaufman, S., Rosset, S., Perlich, C., and Stitelman, O · 2012
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Applied Predictive Modeling
Kuhn, M. and Johnson, K · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2014
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When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging
Whelan, R. and Garavan, H · 2014
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Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models
Blagus, R. and Lusa, L · 2015
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Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises
Bone, D., Goodwin, M. S., Black, M. P., Lee, C.-C., Audhkhasi, K., and Narayanan, S · 2015
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Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement
Collins, G. S., Reitsma, J. B., Altman, D. G., and Moons, K. G · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Opinion: Reproducible research can still be wrong: Adopting a prevention approach
Leek, J. T. and Peng, R. D · 2015
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Estimating the reproducibility of psychological science
Open Science Collaboration · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Data Leakage in Machine Learning, August 2016
Brownlee, J · 2016
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The Treachery of Leakage, August 2016
Fraser, C · 2016
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Forecasting civil conflict along the shared socioeconomic pathways
Hegre, H., Buhaug, H., Calvin, K. V., Nordkvelle, J., Waldhoff, S. T., and Gilmore, E · 2016
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Machine learning, statistical learning and the future of biological research in psychiatry
Iniesta, R., Stahl, D., and McGuffin, P · 2016
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The importance of prediction model validation and assessment in obesity and nutrition research
Ivanescu, A. E., Li, P., George, B., Brown, A. W., Keith, S. W., Raju, D., and Allison, D. B · 2016
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The shape of things to come? Expanding the inequality and grievance model for civil war forecasts with event data
Chiba, D. and Gleditsch, K. S · 2017
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Do the robot: Lessons from machine learning to improve conflict forecasting
Colaresi, M. and Mahmood, Z · 2017
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50 Years of Data Science
Donoho, D · 2017
Cited alongside, same era.
Can civilian attitudes predict insurgent violence? Ideology and insurgent tactical choice in civil war
Hirose, K., Imai, K., and Lyall, J · 2017
Cited alongside, same era.
Prediction and explanation in social systems
Hofman, J. M., Sharma, A., and Watts, D. J · 2017
Cited alongside, same era.
Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
Islam, R., Henderson, P., Gomrokchi, M., and Precup, D · 2017
Cited alongside, same era.
Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure
Roberts, D. R., Bahn, V., Ciuti, S., Boyce, M. S., Elith, J., Guillera-Arroita, G., Hauenstein, S., Lahoz-Monfort, J. J., Schröder, B., Thuiller, W., Warton, D. I., Wintle, B. A., Hartig, F., and Dormann, C. F · 2017
Cited alongside, same era.
Applications of machine learning in animal behaviour studies
Checklist for Artificial Intelligence in Medical Imaging (CLAIM): A Guide for Authors and Reviewers
Mongan, J., Moy, L., and Kahn, C. E · 2020
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Questionable Classification Accuracy Reported in “Designing a Sum of Squared Correlations Framework for Enhancing SSVEP-Based BCIs”
Nakanishi, M., Xu, M., Wang, Y., Chiang, K.-J., Han, J., and Jung, T.-P · 2020
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Training machine learning models on patient level data segregation is crucial in practical clinical applications
Oner, M. U., Cheng, Y.-C., Lee, H. K., and Sung, W.-K · 2020
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Data and its (dis) contents: A survey of dataset development and use in machine learning research
Paullada, A., Raji, I. D., Bender, E. M., Denton, E., and Hanna, A · 2020
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Establishment of Best Practices for Evidence for Prediction A Review
Poldrack, R. A., Huckins, G., and Varoquaux, G · 2020
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Valletta, J. J., Torney, C., Kings, M., Thornton, A., and Madden, J · 2017
Cited alongside, same era.
Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning
Yarkoni, T. and Westfall, J · 2017
Cited alongside, same era.
Nature Methods , 15(9):641–641, September 2018
Easing the burden of code review · 2018
Cited alongside, same era.
Data Leakage, 2018
Becker, D · 2018
Cited alongside, same era.
The accuracy, fairness, and limits of predicting recidivism
Dressel, J. and Farid, H · 2018
Cited alongside, same era.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
Cited alongside, same era.
Meta-analysis and the science of research synthesis
Gurevitch, J., Koricheva, J., Nakagawa, S., and Stewart, G · 2018
Cited alongside, same era.
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Measuring the predictability of life outcomes with a scientific mass collaboration
Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., Datta, D., Davidson, T., Filippova, A., Gilroy, C., Goode, B. J., Jahani, E., Kashyap, R., Kirchner, A., McKay, S., Morgan, A. C., Pentland, A., Polimis, K., Raes, L., Rigobon, D. E., Roberts, C. V., Stanescu, D. M., Suhara, Y., Usmani, A., Wang, E. H., Adem, M., Alhajri, A., AlShebli, B., Amin, R., Amos, R. B., Argyle, L. P., Baer-Bositis, L., Büchi, M., Chung, B.-R., Eggert, W., Faletto, G., Fan, Z., Freese, J., Gadgil, T., Gagné, J., Gao, Y., Halpern-Manners, A., Hashim, S. P., Hausen, S., He, G., Higuera, K., Hogan, B., Horwitz, I. M., Hummel, L. M., Jain, N., Jin, K., Jurgens, D., Kaminski, P., Karapetyan, A., Kim, E. H., Leizman, B., Liu, N., Möser, M., Mack, A. E., Mahajan, M., Mandell, N., Marahrens, H., Mercado-Garcia, D., Mocz, V., Mueller-Gastell, K., Musse, A., Niu, Q., Nowak, W., Omidvar, H., Or, A., Ouyang, K., Pinto, K. M., Porter, E., Porter, K. E., Qian, C., Rauf, T., Sargsyan, A., Schaffner, T., Schnabel, L., Schonfeld, B., Sender, B., Tang, J. D., Tsurkov, E., Loon, A. v., Varol, O., Wang, X., Wang, Z., Wang, J., Wang, F., Weissman, S., Whitaker, K., Wolters, M. K., Woon, W. L., Wu, J., Wu, C., Yang, K., Yin, J., Zhao, B., Zhu, C., Brooks-Gunn, J., Engelhardt, B. E., Hardt, M., Knox, D., Levy, K., Narayanan, A., Stewart, B. M., Watts, D. J., and McLanahan, S · 2020
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@andybeega (Andreas Beger): This is great. One thing I’d add is that for the @DMuchlinski et al data…
Beger, A · 2021
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Reassessing the Role of Theory and Machine Learning in Forecasting Civil Conflict
Beger, A., Morgan, R. K., and Ward, M. D · 2021
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Perspectives on Machine Learning from Psychology’s Reproducibility Crisis
Bell, S. J. and Kampman, O. P · 2021
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Is Theory Useful for Conflict Prediction? A Response to Beger, Morgan, and Ward
Blair, R. A. and Sambanis, N · 2021
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Overinterpretation reveals image classification model pathologies
Carter, B., Jain, S., Mueller, J., and Gifford, D · 2021
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The fundamental principles of reproducibility
Erik Gundersen, O · 2021
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Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”
Filho, A. C., Batista, A. F. D. M., and Santos, H. G. d · 2021
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Datasheets for datasets
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., and Crawford, K · 2021
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Can We Predict Armed Conflict? How the First 9 Years of Published Forecasts Stand Up to Reality
Hegre, H., Nygård, H. M., and Landsverk, P · 2021
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Statistical Significance, p
Imbens, G. W · 2021
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WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B., Haque, I., Beery, S. M., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., and Liang, P · 2021
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How to avoid machine learning pitfalls: a guide for academic researchers
Lones, M. A · 2021
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What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory
Lundberg, I., Johnson, R., and Stewart, B. M · 2021
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An Empirical Study of the Impact of Data Splitting Decisions on the Performance of AIOps Solutions
Lyu, Y., Li, H., Sayagh, M., Jiang, Z. M. J., and Hassan, A. E · 2021
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Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers
Marie, B., Fujita, A., and Rubino, R · 2021
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AI and the Everything in the Whole Wide World Benchmark
Raji, D., Denton, E., Bender, E. M., Hanna, A., and Paullada, A · 2021
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Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Roberts, M., Driggs, D., Thorpe, M., Gilbey, J., Yeung, M., Ursprung, S., Aviles-Rivero, A. I., Etmann, C., McCague, C., Beer, L., Weir-McCall, J. R., Teng, Z., Gkrania-Klotsas, E., Rudd, J. H. F., Sala, E., and Schönlieb, C.-B · 2021
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Putting Psychology to the Test: Rethinking Model Evaluation Through Benchmarking and Prediction
Rocca, R. and Yarkoni, T · 2021
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Do Datasets Have Politics? Disciplinary Values in Computer Vision Dataset Development
Scheuerman, M. K., Hanna, A., and Denton, E · 2021
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Nonreplicable publications are cited more than replicable ones
Serra-Garcia, M. and Gneezy, U · 2021
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Computer vision: algorithms and applications, 2nd ed
Szeliski, R · 2021
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Block cross-validation for species distribution modelling, 2021
Valavi, R., Elith, J., Lahoz-Monfort, J., and Guillera-Arroita, G · 2021
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Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling
Vandewiele, G., Dehaene, I., Kovács, G., Sterckx, L., Janssens, O., Ongenae, F., De Backere, F., De Turck, F., Roelens, K., Decruyenaere, J., Van Hoecke, S., and Demeester, T · 2021
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Dos and Don’ts of Machine Learning in Computer Security
Arp, D., Quiring, E., Pendlebury, F., Warnecke, A., Pierazzi, F., Wressnegger, C., Cavallaro, L., and Rieck, K · 2022
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Genomic Machine Learning Meta-regression: Insights on Associations of Study Features with Reported Model Performance
Barnett, E., Onete, D., Salekin, A., and Faraone, S. V · 2022
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Assessing Methods and Tools to Improve Reporting, Increase Transparency, and Reduce Failures in Machine Learning Applications in Health Care
Garbin, C. and Marques, O · 2022
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Hullman, J., Kapoor, S., Nanayakkara, P., Gelman, A., and Narayanan, A · 2022
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Inflated prediction accuracy of neuropsychiatric biomarkers caused by data leakage in feature selection
Shim, M., Lee, S.-H., and Hwang, H.-J · 2045
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Regions at Risk: Predicting Conflict Zones in African Insurgencies*
Schutte, S · 2049
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