MACHINE LEARNING

Detailed explanation-1: -For supervised learning to work, you need a labeled set of data that the model can learn from to make correct decisions. Data labeling typically starts by asking humans to make judgments about a given piece of unlabeled data.

Detailed explanation-2: -Labeled data is used in supervised learning, whereas unlabeled data is used in unsupervised learning .

Detailed explanation-3: -Datasets are made up of individual examples that contain features and a label. You could think of an example as analogous to a single row in a spreadsheet. Features are the values that a supervised model uses to predict the label. The label is the “answer, ‘’ or the value we want the model to predict.