The following pages link to Random survival forests (Q97881):
Displaying 50 items.
- Predicting risk for adverse health events using random forest (Q5036398) (← links)
- Regression of survival data via twin support vector regression (Q5042113) (← links)
- (Q5054586) (← links)
- Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests (Q5057244) (← links)
- Survival Regression with Accelerated Failure Time Model in XGBoost (Q5057266) (← links)
- <i>L</i><sub>0</sub>-Regularized Learning for High-Dimensional Additive Hazards Regression (Q5058017) (← links)
- Predictive Distribution Modeling Using Transformation Forests (Q5066499) (← links)
- Interval Censored Recursive Forests (Q5084438) (← links)
- A High-Fidelity Model to Predict Length of Stay in the Neonatal Intensive Care Unit (Q5084644) (← links)
- Deep survival algorithm based on nuclear norm (Q5086082) (← links)
- Consistency of survival tree and forest models: splitting bias and correction (Q5089444) (← links)
- Dynamic estimation with random forests for discrete‐time survival data (Q5094314) (← links)
- High dimensional variable selection with clustered data: an application of random multivariate survival forests for detection of outlier medical device components (Q5107399) (← links)
- (Q5149226) (← links)
- (Q5149245) (← links)
- Discrete-time survival trees and forests with time-varying covariates (Q5193314) (← links)
- Time-to-event prediction with neural networks and Cox regression (Q5214221) (← links)
- Automatic model selection for high-dimensional survival analysis (Q5220703) (← links)
- Censoring Unbiased Regression Trees and Ensembles (Q5229919) (← links)
- Greedy outcome weighted tree learning of optimal personalized treatment rules (Q5283293) (← links)
- Heuristic Ranking Classification Method for Complex Large-Scale Survival Data (Q5357735) (← links)
- An overview of techniques for linking high‐dimensional molecular data to time‐to‐event endpoints by risk prediction models (Q5391149) (← links)
- Confidence scores for prediction models (Q5391157) (← links)
- Efron‐Type Measures of Prediction Error for Survival Analysis (Q5449939) (← links)
- The Impact of Churn on Client Value in Health Insurance, Evaluation Using a Random Forest Under Various Censoring Mechanisms (Q5881989) (← links)
- Comments on: ``A random forest guided tour'' (Q5972098) (← links)
- ROC‐guided survival trees and ensembles (Q6047755) (← links)
- Discussion on “Nonparametric variable importance assessment using machine learning techniques” by Brian D. Williamson, Peter B. Gilbert, Marco Carone, and Noah Simon (Q6047784) (← links)
- Binacox: automatic cut‐point detection in high‐dimensional Cox model with applications in genetics (Q6055686) (← links)
- Multiobjective semisupervised learning with a right‐censored endpoint adapted to the multiple imputation framework (Q6068873) (← links)
- Penalized semiparametric Cox regression model on XGBoost and random survival forests (Q6073566) (← links)
- Semiparametric analysis of clustered interval‐censored survival data using soft Bayesian additive regression trees (SBART) (Q6079571) (← links)
- A General Framework for Subgroup Detection via One-Step Value Difference Estimation (Q6079696) (← links)
- Predicting survival outcomes in the presence of unlabeled data (Q6097098) (← links)
- Treatment Effect Estimation Under Additive Hazards Models With High-Dimensional Confounding (Q6107211) (← links)
- Prediction of sports injuries in football: a recurrent time-to-event approach using regularized Cox models (Q6107409) (← links)
- Neural networks for scalar input and functional output (Q6117028) (← links)
- Bayesian survival tree ensembles with submodel shrinkage (Q6121789) (← links)
- A semiparametric promotion time cure model with support vector machine (Q6138594) (← links)
- Dynamic risk prediction triggered by intermediate events using survival tree ensembles (Q6161880) (← links)
- Analyzing Big EHR Data—Optimal Cox Regression Subsampling Procedure with Rare Events (Q6185494) (← links)
- Assessing model prediction performance for the expected cumulative number of recurrent events (Q6205054) (← links)
- A model-free machine learning method for risk classification and survival probability prediction (Q6537809) (← links)
- Learning from high dimensional data based on weighted feature importance in decision tree ensembles (Q6538421) (← links)
- Piecewise exponential models with time-varying effects: estimating mortality after listing for solid organ transplant (Q6541547) (← links)
- On variance estimation of random forests with Infinite-order U-statistics (Q6546441) (← links)
- Accelerated and Interpretable Oblique Random Survival Forests (Q6552541) (← links)
- Sample size and predictive performance of machine learning methods with survival data: a simulation study (Q6560569) (← links)
- Machine Learning vs. Survival Analysis Models: a study on right censored heart failure data (Q6562743) (← links)
- Buckley-James boosting model based on extreme learning machine and random survival forests (Q6563673) (← links)
