Learning accurate and interpretable models based on regularized random forests regression
2014
Background
Many biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance.
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- Machine Reading By IdeaReader
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