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I'm following a tutorial about machine learning basics and there is mentioned that something can be a feature or a label.
From what I know, a feature is a property of data that is being used. I can't figure out what the label is, I know the meaning of the word, but I want to know what it means in the context of machine learning.
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A model defines the relationship between features and label.
A feature is an attribute used to describe each example. ML models are effective because they learn to generate predictions for new examples; this process is called inference. The examples used during the learning process are commonly referred to as training data.
A feature dataset is a collection of related feature classes that share a common coordinate system. Feature datasets are used to facilitate creation of controller datasets (sometimes also referred to as extension datasets), such as a parcel fabric, topology, or utility network.
Labeled data is data that comes with a tag, like a name, a type, or a number. Unlabeled data is data that comes with no tag.
Briefly, feature is input; label is output. This applies to both classification and regression problems.
A feature is one column of the data in your input set. For instance, if you're trying to predict the type of pet someone will choose, your input features might include age, home region, family income, etc. The label is the final choice, such as dog, fish, iguana, rock, etc.
Once you've trained your model, you will give it sets of new input containing those features; it will return the predicted "label" (pet type) for that person.
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