Supervised machine learning is a type of machine learning where the algorithm is trained on a labeled dataset.
Labeled dataset means that each example in the dataset is associated with a specific output value.
The goal of supervised learning is to learn a mapping function that can predict the output value for new, unseen input data.
The labeled dataset used for training is typically split into two subsets: a training set and a test set.
The model is trained on the training set and then evaluated on the test set to check its performance.
The performance of the model is measured using metrics such as accuracy, precision, recall, and F1 score.
There are two main types of supervised learning: classification and regression.
Some popular supervised learning algorithms include decision trees, random forests, logistic regression, and neural networks