An AUC-based permutation variable importance measure for random forests
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CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forestsModeling X Chromosome Data Using Random Forests: Conquering Sex BiasA pharyngeal jaw evolutionary innovation facilitated extinction in Lake Victoria cichlids.Large unbalanced credit scoring using Lasso-logistic regression ensembleGene Expression Profiling of Ewing Sarcoma Tumors Reveals the Prognostic Importance of Tumor-Stromal Interactions: A Report from the Children's Oncology GroupApplying Data Mining Techniques to Improve Breast Cancer Diagnosis.Sensitive Periods for Developing a Robust Trait of Appetitive Aggression.Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer's Dementia.Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensingIntervention in prediction measure: a new approach to assessing variable importance for random forests.Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach.Improving cross-study prediction through addon batch effect adjustment or addon normalization.Are Mortality and Acute Morbidity in Patients Presenting With Nonspecific Complaints Predictable Using Routine Variables?Subsampling versus bootstrapping in resampling-based model selection for multivariable regression.Gender Differences in Machine Learning Models of Trauma and Suicidal Ideation in Veterans of the Iraq and Afghanistan Wars.Systematic assessment of cervical cancer initiation and progression uncovers genetic panels for deep learning-based early diagnosis and proposes novel diagnostic and prognostic biomarkers.Development and validation of a diagnostic model for early differentiation of sepsis and non-infectious SIRS in critically ill children - a data-driven approach using machine-learning algorithms.Disregarding human pre-introduction selection can confound invasive crayfish risk assessmentsUsing field data to assess model predictions of surface and ground fuel consumption by wildfire in coniferous forests of CaliforniaGlobal state and potential scope of investments in watershed services for large cities
P2860
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P2860
An AUC-based permutation variable importance measure for random forests
description
2013 nî lūn-bûn
@nan
2013 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
An AUC-based permutation variable importance measure for random forests
@ast
An AUC-based permutation variable importance measure for random forests
@en
An AUC-based permutation variable importance measure for random forests
@nl
type
label
An AUC-based permutation variable importance measure for random forests
@ast
An AUC-based permutation variable importance measure for random forests
@en
An AUC-based permutation variable importance measure for random forests
@nl
prefLabel
An AUC-based permutation variable importance measure for random forests
@ast
An AUC-based permutation variable importance measure for random forests
@en
An AUC-based permutation variable importance measure for random forests
@nl
P2860
P50
P356
P1433
P1476
An AUC-based permutation variable importance measure for random forests
@en
P2860
P2888
P356
10.1186/1471-2105-14-119
P577
2013-04-05T00:00:00Z
P5875
P6179
1001929149