Support vector machines dominate my prediction modeling nearly every time

Whenever I build a stacking ensemble (be it for classification or regression), a support vector machine nearly always has the lowest error. Quite often, its error will even be lower or equivalent to the entire ensemble with averaged predictions from various models (LDA, GLMs, trees/random forests, KNN, splines, etc.). Yet, I rarely see SMVs used by other people. Is this just because you strip away interpretation for prediction accuracy in SMVs? Is anyone else experiencing this, or am I just …

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