A connectivity matrix molecular representation with random forest feature pooling and adjusted R2 selection predicts pure component properties more accurately than group contribution features in the authors' best cross-validation folds.
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Pure Component Property Estimation Framework Using Explainable Machine Learning Methods
A connectivity matrix molecular representation with random forest feature pooling and adjusted R2 selection predicts pure component properties more accurately than group contribution features in the authors' best cross-validation folds.