Supervised Learning is a methodology in Machine Learning field. In this methodology, an algorithm is developed based on known dataset and known observations from that dataset. Once the algorithm is stable, researchers / developers use it on new but similar dataset to get the observations about that dataset.
In this method, the known relationship between the dataset (training data) and observations (outcome) helps the algorithm to improve. This is kind of a teacher supervising students in learning new technique. And hence this method is referred as “Supervised Learning”. The developer keeps on improving the algorithm until it reaches fairly accurate outcome for all of the training set.
When to use Supervised Learning?
You have a training data available/gathered. After manual analysis, you know the expected outcome. And then you are required to find out the outcome on another similar dataset for which outcome is not yet available. This is an ideal condition to use Supervised Learning.
Tasks involved in Supervised Learning
Typically there are two types of tasks involved in this type of learning.
- Classification: In this case, the algorithm assigns a category to the input dataset. e.g. If the training dataset is a set of files, this algorithm will categorize each file as text file, image file or binary file.
- Regression: In this task, algorithm will predict a numerical value based on training dataset.
Developers often need to consider “bias vs variance trade-off” while determining the accuracy of the algorithm. Sometimes the algorithm consistently produces incorrect output for given input. This is referred to as “bias”. Sometimes, algorithm produces different values for same input. This is called as “variance”. It is usually impossible to have lowest of both bias as well as variance and hence a balance of these two are required. When such balance is reached, developers can start using that algorithm on different datasets and continue to improve.
Example of Supervised Learning:
Let’s say you have 20 photos and each of them are tagged with labels such as person name, location, type of photo. In this case you will develop a model using this information. Once done, you can feed another 20 photos to this model and see if model has “learnt” from earlier dataset.
Fun Fact:
You can find information on Facebook photos “alt” tag – “Image may contain: mountain, sky, outdoor” OR “Image may contain: One Person, Standing, outdoor” etc. This looks like AI running on the photos through Supervised Learning model.
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Related Keywords:
Machine Learning, Unsupervised Learning, Semi-supervised Learning, Active Learning