We compared drug response signatures built using a penalized linear regression model and two non-linear machine learning techniques, random forest and support vector machine.
The non-linear machine learning techniques random forest and support vector machine outperformed the more commonly used elastic net regression in developing precise and robust genomic predictors.
This study aims to compare two machine learning techniques, random forests (RF) and support vector machine (SVM), for real-time radar-derived rainfall forecasting.
In this work, we have selected three well known machine learning algorithms: random forests, support vector machines, and naive Bayes.
In our model, I used well-known algorithms in machine learning such as random forest and K nearest neighborsas well as more technical probabilistic Bayesian models.
In this study, it is shown that a new practical machine learning tool known as random forest (RF) model can be used for variable importance measurements (VIMs) among various physical and mechanical properties of rocks.
We used two different machine learning algorithms to generate classification models, random forests (RF) and logistic regression (LR).
As an alternative to the rule-based algorithm, we applied a machine learning model, random forest [ 42], to classify the GPS data into different time activity categories.
Another machine learning method, the random forest (RF), is detailed in online supplementary materials.
Machine learning methods, including random forest (RF) [ 100] and support vector machines (SVMs) [ 101] use training data to create a classifier (predictor) to classify phenotypes of new samples.
A more detailed list of datasets is provided in Additional file 2. We have used the machine learning library WEKA [34] (v3.7.13) for modeling and selected three well known classifiers: random forests [35], naive Bayes [36] and support vector machines [37].
