For decision tree classification, the variable to predict is most often ordinal-encoded (0, 1, 2 and so on) The numeric predictors do not need to be normalized to all the same range -- typically 0.0 ...

A study of 1,099 patients was conducted to test decision tree-based learning algorithms to predict analgesic consumption and patient-controlled anesthesia control readjustment, according to 7thSpace.

Gradient boosting decision tree (GBDT) for firm failure prediction is proposed. Sensitivity analysis and model interpretability of GBDT are analyzed and validated. GDBT, bagging, Adaboost, Random ...

The main contribution of this paper is a novel tree-based ensemble model for financial distress prediction. We obtain multiple balanced samples with different sampling probabilities. The optimal ...

The results of the study showed the prediction accuracies of total analgesic consumption and PCA requirement by an ensemble of decision trees were 80.9 percent and 73.1 percent, respectively. Decision ...