Unsupervised or Supervised Machine Learning

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I dont quite get what kind of Machine Learning Problem this is.

I do have Data consisting of time and a specific count.

_time count
7:15 190
7:20 240

and so on.

With this Data I would like to create a model and "predict" the count value of specific times. The following Data looks like this:

_time count
7:30
7:35

For this Data i use the trained model and get a valid count out of it. Now I am wondering if it is supervised (because in the model we know the true counts and apply it to another time with unknown count) or if it is unsupervised.

data-science prediction machine-learning model
2 Answers

I will quote an explanation on a blog since I think it is well done and answer your question later.

"There are two main types of learning: supervised and unsupervised. The main difference between the two types is that supervised learning is truth-based. In other words, we have prior knowledge of what the output values of our samples should be. Therefore, the goal of supervised learning is to learn a function that, given a sample of data and the desired results, best approximates the relationship between input and output observable in the data. In contrast, unsupervised learning has no labeled outcomes. Its goal is therefore to infer the natural structure present in a set of data points."

So your problem is supervised, because you have an element of answer, which is a count that you already know, on which you will base yourself to deduct other counts

user_108963380

In the dataset, _time is a feature, and count is the target (also know as "label"). When there is labelled data, it is a supervised machine learning problem.

You can read this article for more details.

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