<ul><li>Adapting machine learning algorithms to handle clusters or batch effects within training datasets is important in various biological applications.</li><li>Ensembling Random Forest learners trained on clusters in datasets with heterogeneous feature distributions improves accuracy and generalizability.</li><li>The Cross-Cluster Weighted Forest approach shows significant benefits over the traditional Random Forest algorithm.</li><li>The approach outperforms classic Random Forest in cancer molecular profiling and gene expression datasets.</li></ul>

Cross-Cluster Weighted Forests

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