Supervised learning is a type of machine learning where the models are trained on questions(input) with answers(output), so if new questions were asked to model then it will give answers related to other answers.
Pros
- This type of learning is very useful if questions and answers are well known.
- The model takes very little time to predict or recognise patterns once the model is trained.
- The model is very useful in predicting anomalies or differences.
Cons
- The answers from the model are limited.
- Training model with a large amount of data is expensive and difficult.
- The model requires lots of questions to learn for giving accurate answers.
This kind of machine learning is very useful if you have enough data to train the model otherwise, the model will predict very inaccurate answers
Applications of supervised learning
- Predicting the weather
- Predicting the salary of the employee
- Prediction this pattern in the image.
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