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The Occam's Razor preference bias tends to drive our learning procedures to consider the simplest models (consistent with the training data). Arguably, linear models are about as simple as they come. Given this preference bias, why would we consider any other kind of model? In our module on non-parametric learning, we also introduced the concept of a kernel, which has the benefit of also allowing us to address non-linearly separable problems as well. Given this, what advantages do we have in considering other model types like nearest neighbor, decision trees/rules, etc.? How will you go about deciding, for your own problems, whether to focus on linear models or some other kind of learning approach?

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Why would we consider any other kind of model?
Answer: Because the bias can be more complex than the bias outlined by linear models. For instance, the attenuation bias from linear models is simple enough. Secondly, the measurement of error in non-linear models does not have a normal distribution and a linear model will fail interpreting this when outputting the results....

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