What characteristic describes Simon's statistical learning method when results are consistent across training datasets?

When a statistical learning method produces consistent results across different training datasets, it is indicative of low variance in the model. Variance refers to the model's sensitivity to fluctuations in the training data; a model with high variance tends to fit the training data very closely, capturing noise as if it were a meaningful signal. This can lead to overfitting, where the model performs well on the training data but poorly on unseen data.

In contrast, a model characterized by low variance will yield similar predictions regardless of the particular training set used. This consistency suggests that the model captures the underlying patterns in the data effectively, rather than being overly influenced by noise specific to any single dataset. This ability to generalize well to unseen data is a crucial feature of robust statistical learning methods.

Thus, when results are consistent across training datasets, it reflects low variance, meaning the model is less likely to be affected by the specific anomalies present in any one training dataset.