Feature importance score based on information gain and feature selection frequency.

Feature importance score based on information gain and feature selection frequency.

<p>Feature importance score based on information gain and feature selection frequency.</p>

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Bibliographic Details
Main Author:	Guomei Cui (20721578) (author)
Other Authors:	Chuanjun Wang (14802747) (author)
Published:	2025
Subjects:	Plant Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machine convolutional neural network commonly used algorithms gradient boosting tree ensemble learning methods real game data decision tree optimization sprint pattern recognition decision tree pattern recognition tree structure existing methods recognition ability data collected xlink "> training data thus reducing test set super parameters study come study aims stride length step frequency sports states simulation environment reasonable selection random forest precision sensors may deviate generalization ability gbt ), future research fitting phenomenon computer simulation competition strategies adaptively adjusting 9 %, 0 %). 0 %)
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Summary:	<p>Feature importance score based on information gain and feature selection frequency.</p>

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