MACHINE LEARNING

Detailed explanation-1: -Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized.

Detailed explanation-2: -Supervised learning describes a class of problem that involves using a model to learn a mapping between input examples and the target variable. Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems.

Detailed explanation-3: -Supervised learning is a machine learning approach that’s defined by its use of labeled datasets. These datasets are designed to train or “supervise” algorithms into classifying data or predicting outcomes accurately. Using labeled inputs and outputs, the model can measure its accuracy and learn over time.

Detailed explanation-4: -Example 1: We may use supervised learning to predict house prices. Data having details about the size of the house, price, the number of rooms in the house, garden and other features are needed. We need data about various parameters of the house for thousands of houses and it is then used to train the data.