Prediction of back break in blasting using random decision trees

2021 ◽  
Author(s):  
Shankar Kumar ◽  
A. K. Mishra ◽  
B. S. Choudhary
Keyword(s):  
Decision Trees ◽  
Random Decision Trees

An Ensemble of Random Decision Trees with Local Differential Privacy in Edge Computing

2021 ◽  
Author(s):  
Xiaotong Wu ◽  
Lianyong Qi ◽  
Jiaquan Gao ◽  
Genlin Ji ◽  
Xiaolong Xu
Keyword(s):  
Decision Trees ◽  
Differential Privacy ◽  
Edge Computing ◽  
Random Decision Trees

scholarly journals Identification of predicted individual treatment effects in randomized clinical trials

2016 ◽  
Vol 27 (1) ◽  
pp. 142-157 ◽  
Author(s):  
Andrea Lamont ◽  
Michael D Lyons ◽  
Thomas Jaki ◽  
Elizabeth Stuart ◽  
Daniel J Feaster ◽  
...  
Keyword(s):  
Decision Trees ◽  
Treatment Effect ◽  
Treatment Effects ◽  
Randomized Clinical Trials ◽  
Controlled Trial ◽  
Physical Symptoms ◽  
Average Treatment Effect ◽  
Individual Treatment ◽  
Monte Carlo Simulation Study ◽  
Random Decision Trees

In most medical research, treatment effectiveness is assessed using the average treatment effect or some version of subgroup analysis. The practice of individualized or precision medicine, however, requires new approaches that predict how an individual will respond to treatment, rather than relying on aggregate measures of effect. In this study, we present a conceptual framework for estimating individual treatment effects, referred to as predicted individual treatment effects. We first apply the predicted individual treatment effect approach to a randomized controlled trial designed to improve behavioral and physical symptoms. Despite trivial average effects of the intervention, we show substantial heterogeneity in predicted individual treatment response using the predicted individual treatment effect approach. The predicted individual treatment effects can be used to predict individuals for whom the intervention may be most effective (or harmful). Next, we conduct a Monte Carlo simulation study to evaluate the accuracy of predicted individual treatment effects. We compare the performance of two methods used to obtain predictions: multiple imputation and non-parametric random decision trees. Results showed that, on average, both predictive methods produced accurate estimates at the individual level; however, the random decision trees tended to underestimate the predicted individual treatment effect for people at the extreme and showed more variability in predictions across repetitions compared to the imputation approach. Limitations and future directions are discussed.

Combining Predictions Under Uncertainty: The Case of Random Decision Trees

2021 ◽  
pp. 78-93
Author(s):  
Florian Busch ◽  
Moritz Kulessa ◽  
Eneldo Loza Mencía ◽  
Hendrik Blockeel
Keyword(s):  
Decision Trees ◽  
Random Decision Trees
Sign in / Sign up

Export Citation Format

Share Document