Practical Big Data Analytics

Unsupervised machine learning

Unsupervised machine learning involves datasets that do not have labeled outcomes. Taking the example of predicting mpg values for cars, in an unsupervised exercise, our dataset would have looked as follows:

If all the outcomes are missing, it would be impossible to know what the values might have been. Recall that the primary premise of machine learning is to use historical information to make predictions on datasets whose outcome is not known. But, if the historical information itself does not have any identified outcomes, then it would not be possible to build a model. Without knowing any other information, ...