RF-ACE multivariate machine learning with heterogeneous data
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Algorithm learning predictor machine learning algorithm

RF-ACE Description

RF-ACE is an efficient implementation of a robust machine learning algorithm for uncovering multivariate associations, building predictors, and predicting novel data, either with classification or regression tree ensembles, from large and diverse data sets. RF-ACE natively handles numerical and categorical data with missing values, and in feature selection potentially large quantities of noninformative features are handled gracefully utilizing artificial contrast features, bootstrapping, and p-value estimation. The application implements both Gradient Boosting Tree (GBT) and Random Forest (RF) algorithms. Main features: Data can be provided in various formats Estimates default model parameters based on dimensions of input data Extensive support for customization Importance score is normalized and is thus comparable across parallel RF-ACE runs having different target features: Useful in construction of "all-vs-all" association maps Importance score is further translated to a p-value based on empirical background model and t-test Implements GBT and RF for prediction Intuitive interface: rf-ace-filter performs feature selection rf-ace-build-predictor builds predictor based on training data rf-ace-predict makes predictions with novel data

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