It is one of the types of Machine Learning (ML). In Supervised learning output supervised the input. It is used for approximating the input/output behavior of complex systems. The task of the supervised learner is to predict the output behavior of a system for any set of input values, after an initial training phase.
Supervised learning generates a function that maps inputs to desired outputs (also called labels, because they are often provided by human experts labeling the training examples)
Definition of basics used:
The training set: It is the data that the classifier uses to learn how to classify the data.
Validation Set: It is used to feed the already trained model in order to get an error rate that can help us identify the classifier’s performance and accuracy
Cross-validation: Cross-validation is one of several approaches to estimating how well the model you've just learned from some training data is going to perform on future as-yet-unseen data.
Steps of Supervised Learning:
Step 1: Prepare training data
Step 2: Create a training set and a validation set
Step 3: Train the model
Step 4: Cross-validation
Step 5: Validate the model’s performance
Now it’s ready.
