Evaluation of classifier performance and the impact of learning algorithm parameters

Much research has been done in the fields of classifier performance evaluation and optimization. This work summarizes this research and tries to answer the question if algorithm parameter tuning has more impact on performance than the choice of algorithm. An alternative way of evaluation; a measure function is also demonstrated. This type of evaluation is compared with one of the most accepted methods; the cross-validation test. Experiments, described in this work, show that parameter tuning often has more impact on performance than the actual choice of algorithm and that the measure function could be a complement or an alternative to the standard cross-validation tests