Papers, conference presentations, and talks often don't discuss how the models were actually deployed and maintained in a production environment. In this section, we'll look into some aspects that should be taken into consideration.
Machine learning in real life
Noisy data
In practice, data typically contains errors and imperfections due to various reasons such as measurement errors, human mistakes, and errors of expert judgment in classifying training examples. We refer to all of these as noise. Noise can also come from the treatment of missing values when an example with unknown attribute value is replaced by a set of weighted examples corresponding to the probability distribution of the missing value. The typical...
