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We demonstrate that, for a range of state-of-the-art machine learningalgorithms, the differences in generalisation performance obtained usingdefault parameter settings and using parameters tuned via cross-validation canbe similar in magnitude to the differences in performance observed betweenstate-of-the-art and uncompetitive learning systems. This means that fair andrigorous evaluation of new learning algorithms requires performance comparisonagainst benchmark methods with best-practice model selection procedures, ratherthan using default parameter settings. We investigate the sensitivity of threekey machine learning algorithms (support vector machine, random forest androtation forest) to their default parameter settings, and provide guidance ondetermining sensible default parameter values for implementations of thesealgorithms. We also conduct an experimental comparison of these threealgorithms on 121 classification problems and find that, perhaps surprisingly,rotation forest is significantly more accurate on average than both randomforest and a support vector machine.

On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms