How to differentiate between supervised learning (supervised learning) and unsupervised learning (unsupervised learning)

supervised learning : In short, given a certain training sample (it is important to note that the sample is both data and data corresponding to the results), using this sample training to get a model (can be said to be a function), and then use this model to map all the input to the corresponding output, The output is then simply judged so that the problem of classification (or regression) is achieved. Simply make a distinction, the classification is discrete data, regression is continuous data.

Non-supervised learning : Similarly, the sample was given, but the sample was only data, but there was no corresponding result, requiring direct analysis modeling of the data.

For example, we go to a painting exhibition, we are completely ignorant of the art, but after appreciating a lot of works, we can also divide them into different factions (such as which is more hazy, which is more realistic, even if we do not know what is called the hazy faction, what is called realism, but at least we can divide them into two categories). Unsupervised learning the typical example is clustering, the purpose of clustering is to bring together similar things, and we don't care what this class is, so a clustering algorithm usually only needs to know how to calculate the similarity to begin to work.

"For example, when buying a house, to the housing area and its corresponding price, the analysis, this is called supervised learning, but to the area, not to the price, is called unsupervised learning." supervision, which means giving a standard as ' supervision ' (or understanding as a limitation). That is, after modeling there is a standard to measure your right and wrong, non-supervision is not the standard, after the data clustering, there is no standard to measure it. "

How to differentiate between supervised learning (supervised learning) and unsupervised learning (unsupervised learning)