Supervised Learning

Supervised Learning is a type of machine learning where a model is trained on a labeled dataset, meaning that each training example is paired with an output label. The objective of supervised learning is to learn a mapping from inputs to outputs based on this labeled data, allowing the model to make predictions or classify new, unseen data points effectively.In supervised learning, the algorithm iteratively adjusts its parameters to minimize the difference between its predictions and the actual output labels in the training data, typically using a loss function. Common algorithms used in supervised learning include linear regression, logistic regression, decision trees, support vector machines, and neural networks, among others.Supervised learning is widely used in applications such as classification tasks (e.g., spam detection, image recognition) and regression tasks (e.g., predicting house prices, forecasting sales). The success of a supervised learning model is often evaluated using metrics like accuracy, precision, recall, and F1 score for classification tasks, or mean squared error for regression tasks.