ExhaustiveSearch - A Fast and Scalable Exhaustive Feature Selection Framework
The goal of this package is to provide an easy to use, fast and scalable exhaustive search framework. Exhaustive feature selections typically require a very large number of models to be fitted and evaluated. Execution speed and memory management are crucial factors here. This package provides solutions for both. Execution speed is optimized by using a multi-threaded C++ backend, and memory issues are solved by by only storing the best results during execution and thus keeping memory usage constant.
Last updated 4 years ago
aicexhaustive-searchfeature-selectionlinear-regressionlogistic-regressionmachine-learningmodel-selectionmseopenblascpp
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