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# Example learning task, we report the lines of code needed to perform a PCA followed by a SVM classification.
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# In particular, we detail the operational steps needed to project the samples of a UCI5 dataset on the cartesian plane
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# generated by the first two principal components, to train a kernel SVM on the projected data and to test
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# the trained model on the same data.
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#
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# The dataset chosen for this dimensionality reduction example is the Iris dataset, collecting 150 observations
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# of 3 different classes of iris flowers, each described by 4 attributes.
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# As a working example illustrating the use of the library in a simple machine
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##http://www.researchgate.net/publication/221667696_mlpy_Machine_Learning_Python
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iris.shape # 2d numpy array, 150 observations and 4 attributes (150, 4)

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