How does scaling a learning curve influence the curve fitting process?
C. van den Oudenhoven (TU Delft - Electrical Engineering, Mathematics and Computer Science)
O. Taylan Turan (TU Delft - Pattern Recognition and Bioinformatics)
C. Yan (TU Delft - Pattern Recognition and Bioinformatics)
Tom Viering (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
Learning curves show the learning rate of a clas- sifier by plotting the dataset size used to train the classifier versus the error rate. By extrapolating these curves it is possible to predict how well the classifier will perform when trained on dataset sizes that are currently not available. This can be useful when trying to determine which classifier to select when dealing with a classification problem. Ob- taining these learning curves is usually done by fit- ting a parametric model to the learning data. This paper analyzes the potential of fitting the curve in a different space scaling the fitting data. This is done by analyzing the accuracy of the fit and the frequency of the fit succeeding. Our main findings are that log scaling produces better MSEs than not scaling, while exponential scaling is inconclusive.