AOCL-DA offers the option to work with histograms constructed from the training data rather than the original data set. By discretizing continuous feature values into bins, histograms enable rapid calculations of impurity measures and split thresholds, thus reducing the computational overhead associated with finding optimal splits during training. Furthermore, this histogram-based approach also helps to stabilize the model by smoothing out noise in the data thus mitigating the risk of overfitting to individual data points.