Although there are many promising applications of a learning curve in machine learning, such as model selection, we still know very little about what factors influence their behaviours. The aim is to study the impact of the inherent characteristics of the datasets on the learning shapes, which are noise, discretized input and dimensionality. We trained two classifiers with a panoply of datasets for the investigation to see how the learning curve behaves under different circumstances. Firstly, we found that the shapes of the curves varied with different levels of noise injected into the original datasets. Secondly, using the equal width interval binning technique to discretize continuous features did not make the classifiers learn exponentially but caused the learning curves to behave unpredictably; thus, it does not transform the continuous problem into the easier class of problems mentioned in [1]. Finally, the more dimension we reduced using the PCA technique, the learning curve showed strange behaviours.

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