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Next, the feature selection was performed using two different steps: the first step was performed where a correlation matrix was applied to identify the most correlated features and reduce dimensionality problems within the dataset, and in the second step, the random forest (RF) classifier was used to identify non-linear relationships between the most correlated features and thus build a more comprehensive panel of predictors of the football players’ recovery states

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