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Confidence Intervals for Random Forests in Python

Kivan Polimis, Ariel Rokem, Bryna Hazelton
2017 Journal of Open Source Software  
is a Python module for calculating variance and adding confidence intervals to scikit-learn random forest regression or classification objects.  ...  Prediction variability can illustrate how influential the training set is for producing the observed random forest predictions and provides additional information about prediction accuracy. forest-confidence-interval  ...  Confidence Intervals for Random Forests in Python. Journal of Open Source Software, 2(19), 124, doi:10.21105/joss.00124 4  ... 
doi:10.21105/joss.00124 fatcat:jemmwmuoozgy3lpohexruo4zaa

Meta-analysis parameters computation: a Python approach to facilitate the crossing of experimental conditions [article]

Flavien Quijoux, Charles Truong, Aliénor Vienne-Jumeau, Laurent Oudre, François BERTIN-HUGAULT, Philippe ZAWIEJA, Marie LEFEVRE, Pierre-Paul VIDAL, Damien RICARD
2020 arXiv   pre-print
It is necessary to maintain a high level of homogeneity in the aggregation of data collected from a systematic literature review.  ...  This article aims at proposing a Python programming code containing several functions allowing the analysis and rapid visualization of data from many studies, while allowing the possibility of cross-checking  ...  This collaboration between Centre Borelli of Paris Descartes University, and ORPEA group is framed in the French conventions for Industrial Training by the Research (CIFRE) managed by the National Association  ... 
arXiv:2007.07799v1 fatcat:lwom7ihchffefeuzs3p4kwe4km

Native Burmese pythons exhibit site fidelity and preference for aquatic habitats in an agricultural mosaic

Samantha Nicole Smith, Max Dolton Jones, Benjamin Michael Marshall, Surachit Waengsothorn, George A. Gale, Colin Thomas Strine
2021 Scientific Reports  
We set out to explore the space use and habitat selection of Burmese pythons (Python bivittatus) in a heterogenous, agricultural landscape within the Sakaerat Biosphere Reserve, northeast Thailand.  ...  In general, Burmese pythons restricted movement and selected aquatic habitats but did not avoid potentially dangerous land use types like human settlements.  ...  We commend the Udom Sab/Hook 31 Rescue team for working towards human-snake conflict mediation and thank them for assisting in the capture of several Burmese pythons.  ... 
doi:10.1038/s41598-021-86640-1 pmid:33782524 fatcat:f67eojrarrf6je2vhpkwobkmvq

Autorank: A Python package for automated ranking of classifiers

Steffen Herbold
2020 Journal of Open Source Software  
Additional aspects also matter, e.g., effect sizes, confidence intervals, and the decision whether it is appropriate to report the mean value and standard deviation, or whether the median value and the  ...  Regardless, the correct use of Demšar's guidelines is hard for non-experts in statistics.  ...  Based the post-hoc Nemenyi test, we assume that there are no significant differences within the following groups: Naive Bayes, Random Forest, RBF SVM, and Neural Net; Random Forest, RBF SVM, Neural Net  ... 
doi:10.21105/joss.02173 fatcat:42kr3xizfjfgbmo7f2thqwdooy

Predicting post-experiment fatigue among healthy young adults: Random forest regression analysis

Eun-Young Mun, Feng Geng
2019 Psychological test and assessment modeling  
The current study utilized a random forest regression analysis to predict post-experiment fatigue in a sample of 212 healthy participants (mean age = 20.5, SD = 2.21; 52% women) between the ages of 18  ...  A random forest regression analysis can relatively easily be implemented with a built-in cross-validation function and reveal a web of connections undergirding health behavior and risks.  ...  Ray, and other members of the Sensation and Emotion Lab for their help with data collection.  ... 
pmid:32038903 pmcid:PMC7007183 fatcat:c4mwyssavnf3fed3prqmm5ofje

Spatial ecology of invasive Burmese pythons in southwestern Florida

Ian A. Bartoszek, Brian J. Smith, Robert N. Reed, Kristen M. Hart
2021 Ecosphere  
Nest site selection was highest for pythons at an elevation of 1.7 m with nesting hotspots concentrated on the borders of urban and agricultural areas or in sandy forested upland habitats.  ...  Here, we present results from a study using radiotelemetry to quantify movements and habitat use patterns of 25 adult Burmese pythons in southwestern Florida, USA, for average periods of 814 d (range:  ...  generate 95% confidence intervals.  ... 
doi:10.1002/ecs2.3564 fatcat:kkx3mwxnvff77lyks5eq5d5tqe

Word2Vec inversion and traditional text classifiers for phenotyping lupus

Clayton A. Turner, Alexander D. Jacobs, Cassios K. Marques, James C. Oates, Diane L. Kamen, Paul E. Anderson, Jihad S. Obeid
2017 BMC Medical Informatics and Decision Making  
Conclusions: Our results suggest that a shallow neural network with CUIs and random forests with both CUIs and BOWs are the best classifiers for this lupus phenotyping task.  ...  Therefore, currently, the shallow neural networks and random forests are the desirable classifiers.  ...  Acknowledgements We would like to acknowledge Adrian Michael Nida for his early work on this project during his graduate training at MUSC.  ... 
doi:10.1186/s12911-017-0518-1 pmid:28830409 pmcid:PMC5568290 fatcat:ptcp4xs37bbydnpjcyodltb7s4

Teaching reproducible research for medical students and postgraduate pharmaceutical scientists [article]

Andreas D. Meid
2020 arXiv   pre-print
RR working habits are not only beneficial for ourselves or our students, but also for other researchers within an institution, for scientific partners, for the scientific community, and eventually for  ...  In this article, I focus on this working example to emphasize the manifold facets of RR, to provide possible explanations and solutions, and argue that harmonized curricula for (quantitative) clinical  ...  on bootstrapped samples to derive estimates for their 76 mean and 95 % confidence intervals.  ... 
arXiv:2012.03554v1 fatcat:t5pagce2l5agviazlbknfc2ghu

Optimizing Prediction Intervals by Tuning Random Forest via Meta-Validation [article]

Sean Bayley, Davide Falessi
2018 arXiv   pre-print
However, to our best knowledge, no study has investigated the need to, and the manner in which one can, tune Random Forest for optimizing prediction intervals this paper aims to fill this gap.  ...  This paper investigates which, out of eight validation techniques, are beneficial for tuning, i.e., which automatically choose a Random Forest configuration constructing prediction intervals that are reliable  ...  In order to support the usability and replicability of this study, we provide a Python package for tuning and meta-tuning Random Forests PIs.  ... 
arXiv:1801.07194v1 fatcat:heei36uvjngsnpu2hrpr4445sm

Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout [article]

Isidro Cortes-Ciriano, Andreas Bender
2019 arXiv   pre-print
of values than those computed with Conformal Predictors generated using Random Forest (RF) models.  ...  ., the fraction of instances whose true value lies within the predicted interval strongly correlates with the confidence level) and efficient, as the predicted confidence intervals span a narrower set  ...  -Random Forests Cross-Conformal Predictors generated using Random Forests were used as a baseline for comparison. RF-based Cross-Conformal Predictors were generated as previously reported 34, 59 .  ... 
arXiv:1904.06330v1 fatcat:eghzja34hbbqtfztlvq6oaj3bm

Salary Estimator using Data Science

Winner Walecha and Dr. Bhoomi Gupta
2020 International journal of modern trends in science and technology  
Techniques like linear regression, lasso regression, random forest regressors are optimised using GridsearchCV to reach the best model.  ...  This paper presents a salary prediction system using the job listings from an employment website, in this case  ...  EDA can help in answering questions about categorical variables, confidence intervals and standard deviations.  ... 
doi:10.46501/ijmtst061259 fatcat:qpdnrj5d2rh5ne6mlpxq3dxl2m

Easyml: Easily Build And Evaluate Machine Learning Models [article]

Woo-Young Ahn, Paul Hendricks, Nathaniel Haines
2017 bioRxiv   pre-print
The package provides standardized recipes for regression and classification algorithms in R and Python and implements them in a functional, modular, and extensible framework.  ...  This package currently implements recipes for several common machine learning algorithms (e.g., penalized linear models, random forests, and support vector machines) and provides a unified interface to  ...  Multivariate patterns of impulsivity measures predicting cocaine dependence in (A) R and (B) Python. Error bars indicate 95% confidence intervals.  ... 
doi:10.1101/137240 fatcat:lnwjjzapcrc7fgcifl54gvwqvy

Machine Learning-Based Prediction of Korean Triage and Acuity Scale Level in Emergency Department Patients

Sae Won Choi, Taehoon Ko, Ki Jeong Hong, Kyung Hwan Kim
2019 Healthcare Informatics Research  
The models with the highest area under the receiver operating characteristic curve (AUROC) were the random forest and XGBoost models trained on the entire dataset (AUROC = 0.922, 95% confidence interval  ...  Information collected during triage was used in the analysis. Logistic regression, random forest, and XGBoost were used to predict the KTAS level.  ...  interval.  ... 
doi:10.4258/hir.2019.25.4.305 pmid:31777674 pmcid:PMC6859273 fatcat:rxgwuaqrhrcslgqqkworp2wvly

Concepts and Applications of Conformal Prediction in Computational Drug Discovery [article]

Isidro Cortés-Ciriano, Andreas Bender
2019 arXiv   pre-print
For instance, at a confidence level of 90% the true value will be within the predicted confidence intervals in at least 90% of the cases.  ...  Finally, we describe the current limitations in the field, and provide a perspective on future opportunities for CP in preclinical and clinical drug discovery.  ...  For instance, at a confidence level of 80%, the confidence intervals would contain the true value in at least 80% of the cases.  ... 
arXiv:1908.03569v1 fatcat:evr67kv32ve4dg6yd3iqg5ukd4

Data tables and histology from Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms

Lakshmi Y. Sujeeun, Nowsheen Goonoo, Honita Ramphul, Itisha Chummun, Fanny Gimié, Shakuntala Baichoo, Archana Bhaw-Luximon
Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%.  ...  The predictive model was also correlated with in vivo data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.  ...  Table S2 S2 Nanoscaffolds found in the 95% confidence interval while plotting the actual values from the test dataset against predicted values from the random forest model.  ... 
doi:10.6084/m9.figshare.13396472.v2 fatcat:4b3ebwb3onbercyz5cmyd256ze
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