AI framework intended to solve a problem of bias-variance tradeoff
for supervised learning methods in real-life applications. The AI
framework comprises of bootstrapping to create multiple training
and testing data sets with various characteristics, design and
analysis of statistical experiments to identify optimal feature
subsets and optimal hyper-parameters for ML methods, data
contamination to test for the robustness of the classifiers. Key
Features: Using ML methods by itself doesn't ensure building
classifiers that generalize well for new data Identifying optimal
feature subsets and hyper-parameters of ML methods can be resolved
using design and analysis of statistical experiments Using a
bootstrapping approach to massive sampling of training and tests
datasets with various data characteristics (e.g.: contaminated
training sets) allows dealing with bias Developing of SAS-based
table-driven environment allows managing all meta-data related to
the proposed AI framework and creating interoperability with R
libraries to accomplish variety of statistical and machine-learning
tasks Computer programs in R and SAS that create AI framework are
available on GitHub
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