Data mining researchers and practitioners often use general rules of thumb or common data mining wisdom, those are so called data-mining myths.
Even though, these myths are not always proven or sufficiently proven for general non-specific cases.
Therefore, two data-mining myths are closely examined in this study.
The two myths that were investigated are "Data preparation is more important than algorithm selection" and "Non-linear models perform better".