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Classification algorithms are the most commonly used data mining models that are widely used to extract valuable knowledge from huge amounts of data. The criteria used to evaluate the classifiers are mostly accuracy, computational complexity, robustness, scalability, integration, comprehensibility, stability, and interestingness. This study compares the classification of algorithm accuracies, speed (CPU time consumed) and robustness for various datasets and their implementation techniques. The data miner selects the model mainly with respect to classification accuracy; therefore, the performance of each classifier plays a crucial role for selection. Complexity is mostly dominated by the time required for classification. In terms of complexity, the CPU time consumed by each classifier is implied here. The study first discusses the application of certain classification models on multiple datasets in three stages: first, implementing the algorithms on original datasets; second, implementing the algorithms on the same datasets where continuous variables are discretised; and third, implementing the algorithms on the same datasets where principal component analysis is applied. The accuracies and the speed of the results are then compared. The relationship of dataset characteristics and implementation attributes between accuracy and CPU time is also examined and debated. Moreover, a regression model is introduced to show the correlating effect of dataset and implementation conditions on the classifier accuracy and CPU time. Finally, the study addresses the robustness of the classifiers, measured by repetitive experiments on both noisy and cleaned datasets.