Supervised learning

Supervised learning is a type of machine learning where an algorithm learns from labeled training data to make predictions or decisions without being explicitly programmed. In supervised learning, the algorithm is provided with a dataset that includes input-output pairs, and it learns to map inputs to corresponding outputs. The goal is to find a mapping function that can generalize from the training data to make accurate predictions on new, unseen data.

Here are the key components of supervised learning:

1. Training Data: This is the labeled dataset used to train the model. Each data point in the training set consists of input features and their corresponding target or output values.
2. Model: The model is the algorithm or system that learns from the training data to make predictions. It represents the hypothesis or mapping function that relates inputs to outputs. Various machine learning algorithms, such as linear regression, decision trees, neural networks, and support vector machines, can be used as models in supervised learning.
3. Loss or Cost Function: A loss function measures the difference between the predicted output and the actual target. The goal is to minimize this function, which helps the model learn to make accurate predictions.
4. Optimization Algorithm: Optimization algorithms, like gradient descent, are used to update the model's parameters to minimize the loss function.
5. Prediction: Once the model is trained, it can be used to make predictions or classifications on new, unseen data.

Supervised learning can be further divided into two main types:

• Regression: In regression, the goal is to predict a continuous output or numerical value. For example, predicting house prices based on features like square footage and number of bedrooms is a regression task.
• Classification: In classification, the goal is to assign input data to one of several predefined categories or classes. For example, classifying emails as spam or not spam is a classification task.

Supervised learning is widely used in various applications, including image recognition, natural language processing, recommendation systems, medical diagnosis, and more. It's a fundamental and powerful paradigm in machine learning because it allows models to learn from historical data and make data-driven decisions.