

![0_image_0.png](0_image_0.png)

incorporating from one to 100 decision trees or neural networks. The error rate graphed is simply the average of the error rates of the 23 data **sets. The alternative** of averaging the error over all data points (i.e., weighting **a data set's error rate** by its sample size) produces similarly shaped curves.
rate. Thus a high correlation (i.e., one near 1.0) suggests that two methods are consistent in the domains in which they have the greatest impact on test-set error reduction.

Table 3 provides numerous interesting insights. The first is **that the neural-network**
ensemble methods are strongly correlated with one another and the decision-tree ensemble methods are strongly correlated with one another; however, **there is less correlation between any neural-network ensemble method and any decision-tree ensemble method. Not**
surprisingly, Ada-boosting and Arcing are strongly correlated, even across different component learning algorithms. This suggests that Boosting's effectiveness depends more on the data set than whether the component learning algorithm is a neural network or decision tree. Bagging on the other hand, is not correlated across component learning algorithms.

These results are consistent with our later claim that while **Boosting is a powerful ensemble**
method, it is more susceptible to a noisy data set than Bagging.