Building Machine Learning Systems with Python - Richert, Coelho

Chapter 5
Method mean(scores) stddev(scores)
LogReg C=1.00 0.6290 0.03270
90NN 0.6280 0.02777
We have seen the accuracy for the different values of the regularization parameter
C. With it, we can control the model complexity, similar to the parameter k for the
nearest neighbor method. Smaller values for C result in a higher penalty, that is,
they make the model more complex.
A quick look at the bias-variance chart for our best candidate, C = 0.1, shows
that our model has high bias—test and train error curves approach closely but
stay at unacceptably high values. This indicates that logistic regression with the
current feature space is under-fitting and cannot learn a model that captures the
data correctly.
So what now? We switched the model and tuned it as much as we could with our
current state of knowledge, but we still have no acceptable classifier.
It seems more and more that either the data is too noisy for this task or that our set
of features is still not appropriate to discriminate the classes that are good enough.
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