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Hands-On Machine Learning with C# by Matt R. Cole

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Data preparation

As we said previously, there is simply no substitute for data quality. Is there data that is missing, malformed, or incorrect? And let's not forget about another term you'll get familiar with, data outliers. Those are the nasty little pieces of data that simply don't fit nicely with the rest of your data! Do you have those? If so, should they be there, and if so, how will they be treated? If you are not sure, here's what a data outlier might look like if you are plotting your data:

In statistics, an outlier is an observation point that is distant from other observations, sometimes very much so, sometimes not. The outlier itself ...

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