By by Ulf Johansson.
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Additional info for Obtaining accurate and comprehensible data mining models : an evolutionary approach
First of all, fewer instances are available for actual training. 7 Evaluation and comparison of classifiers 31 holdout sets. A relatively small training set increases the variance of the model, while a relatively small holdout set increases the confidence interval of the estimated accuracy. In addition, the two sets are no longer independent. Since both parts were drawn from the original data set, the fact that one class is underrepresented in the training set automatically means that it is overrepresented in the holdout set.
The closer r is to 0 the smaller the correlation. A perfect correlation exists when r = ±1. The coefficient of determination (R2), defined as the square of the correlation coefficient, thus is a real value R2∈ [0, 1]. R2 is interpreted as the proportion of the variance in the dependent variable explained by the model; see equation (14). 4 Predictive classification Predictive classification is the task of learning a target function f that maps each instance x to one of the predefined class labels y.
P. 1 Terminology for the different data sets used during data mining When performing data mining, the data set is normally split in several parts. Each part is used differently and for different purposes. Unfortunately, the names of the data sets are not fully standardized. A first, important, division is to separate the parts of the data set used to build the model from the parts used to evaluate the model. • The training set is the part of the data set used to build the model. • The validation set is used for model (or parameter) selection.