Comparative analysis of classification algorithms

Machine learning algorithms are widely used in classification problems. Certainly, recognition quality of algorithms is important indicator, but the ability of the algorithm to learn is more significant. In this work the learning curves experiment was performed in order to identify which of the three learning rates occur when training the machine learning algorithms: overfitting, perfect case and underfitting. Neural Network, k-Nearest Neighbors and Naïve Bayes were chosen for this experiment, since their results in previous experiments were reasonable for the log data. Also this paper contains a comparative analysis of those recognition algorithms applied to the log data of Inkai uranium deposits in Kazakhstan.