We have looked at the methods that machine learning has and how these methods recognize patterns. In this section, we'll see which flow is taken, or has to be taken, by data mining using machine learning. A decision boundary is set based on the model parameters in each of the machine learning methods, but we can't say that adjusting the model parameters is the only thing we have to care about. There is another troublesome problem, and it is actually the weakest point of machine learning: feature engineering. Deciding which features are to be created from raw data, that is, the analysis subject, is a necessary step in making an appropriate classifier. And doing this, which is the same as adjusting the model parameters, also requires a massive amount of trial and error. In some cases, feature engineering requires far more effort than deciding a parameter.
Thus, when we simply say "machine learning," there are certain tasks that need to be completed in advance...
