

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

The examples studied in this test show that in most cases the sensitivity of the algorithm does not depend on the number of irrelevant attributes in the system. On the other hand the selectivity of the algorithm is decreasing with number of attributes, although this decrease is rather slow. One should note that the high variance of the artificial attributes, which were drawn from the uniform distribution, facilitates creation of fluctuations that may, in conjunction with other attributes, lead to creation of false rules. The observed tendencies should be considered a worst-case scenario. As demonstrated in the next section, these effects are smaller or even non-existent, when the artificial variables are drawn from more realistic distributions.

## 4.3 Gene Expression Data

The first major observation driven from the results of gene expression experiment is that the sensitivity of the algorithm significantly increases with the increasing number of trees grown in the random forest, see Figure 5. The number of important attributes grows logarithmically with increasing number of trees in the forest. It starts from 82 relevant attributes found for the default forest size (500 trees). Then about twenty five additional relevant attributes are identified per doubling of the number of trees, up to fifty thousand trees. The last doubling, from fifty to one hundred thousand trees brings in only 5 new attributes. The parallel control experiment was performed using the semi-synthetic version of the Golub set, where the original attributes were extended with one thousand artificial contrast variables. In all fifteen repetitions of the control experiment not even a single contrast variable was ever deemed relevant by the algorithm,