CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data

<p>Comparison between CloudForest and scikit-learn in terms of prediction performance (<b>a</b>) and training time (<b>b</b>) for a TCGA dataset with varying numbers of missing values (x-axis). For scikit-learn missing values are imputed before RF analysis, whereas CloudForest natively handles missing values without imputation. The time necessary for imputation for scikit-learn is not included in the training times depicted.</p

CloudForest: A Scalable and Efficient Random Forest Implementation for Biological Data

<p>Comparison between CloudForest and other RF implementations in terms of prediction performance (<b>a</b>) and training time (<b>b</b>). The RFs consisted of 500 trees and were trained using the same standard parameter settings for all implementations.</p