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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
In [[machine learning]], [[example]]s or [[training data]] are [[input]] data and the corresponding desired [[output]]. A single [[example]] is the values of one row of [[ | In [[machine learning]], [[example]]s or [[training data]] are [[input]] data and the corresponding desired [[output]]. A single [[example]] is the values of one row of [[feature]]s and possibly a label. Examples in [[supervised learning]] fall into two general categories: labeled examples and unlabeled examples. Labeled examples comprise one or more [[feature]]s and a [[label]]. Labeled examples are used during [[training]]. On the other hand, unlabeled examples consist of features but no label. Unlabeled examples can be utilized during training and [[inference]]. | ||
==What is an example in machine learning?== | ==What is an example in machine learning?== | ||
An example in machine learning refers to a pair of input and output values used to train a model. The input value is made up of [[ | An example in machine learning refers to a pair of input and output values used to train a model. The input value is made up of [[feature]]s or attributes that describe an object or phenomenon, while the output value serves as its [[label]] or [[class]]. For instance, spam detection systems typically take email messages (input) as their label (output), which could either be "spam" or "not spam." Similarly, image recognition systems take pictures as inputs and assign them labels describing what the image depicts. Suppose we collected a dataset of 2000 apartments with their features and prices. The features and the price of a single apartment would be an example. | ||
{| class="wikitable" | {| class="wikitable" | ||
|+ 3 [[labeled example]]s | |||
|- | |- | ||
! colspan="3"| Features | ! colspan="3"| Features | ||
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|} | |} | ||
{| class="wikitable" | {| class="wikitable" | ||
|+ 3 [[unlabeled example]]s | |||
|- | |- | ||
! colspan="3"| Features | ! colspan="3"| Features | ||
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By providing the computer with multiple examples, it can begin to recognize patterns in data and even learn to recognize fruit on its own. It's like how you can recognize a dog even if you haven't seen that particular breed before because you know its look from all of your previous pictures and experiences with dogs. | By providing the computer with multiple examples, it can begin to recognize patterns in data and even learn to recognize fruit on its own. It's like how you can recognize a dog even if you haven't seen that particular breed before because you know its look from all of your previous pictures and experiences with dogs. | ||
[[Category:Terms]] [[Category:Machine learning terms]] | [[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | ||