Package QSTK :: Package qstklearn :: Module mldiagnostics :: Class MLDiagnostics
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Class MLDiagnostics

source code


This class can be used to produce learning curves.
These are plots of evolution of Training Error and Cross Validation Error across lambda(in general a control param for model complexity).
This plot can help diagnose if the ML algorithmic model has high bias or a high variance problem and can
thus help decide the next course of action.
In general, ML Algorithm is of the form,
        Y=f(t,X) + lambdaVal*|t|
        where Y is the output, t is the model parameter vector, lambdaVal is the regularization parameter.
        |t| is the size of model parameter vector.

Instance Methods [hide private]
 
__init__(self, learner, Xtrain, Ytrain, Xcv, Ycv, lambdaArray) source code
 
avgsqerror(self, Y, Ypred) source code
 
plotCurves(self, filename) source code
 
runDiagnostics(self, filename) source code