Supervised Learning is a type of Machine Learning (ML) in which an algorithm learns to map input data to corresponding output labels based on a labeled training dataset. In supervised learning, the algorithm is provided with a set of input-output pairs, also known as examples or instances, where the correct output (label) is already known.
The goal of supervised learning is to learn a mapping function that can accurately predict the output labels for new, unseen inputs.
The process of supervised learning involves the following steps:
- Dataset Preparation: Collect and prepare a labeled dataset, which includes input data along with their corresponding correct output labels.
- Training Phase: The algorithm uses the training dataset to learn the relationship between input data and output labels. It adjusts its internal parameters to minimize the difference between its predictions and the actual labels.
- Validation: A validation dataset might be used during training to assess the model’s performance on data it hasn’t seen before. This helps prevent overfitting (when the model performs well on training data but poorly on new data).
- Testing Phase: After training, the model is evaluated on a separate test dataset that it has not seen before. This assesses how well the model generalizes to new, unseen data.
- Model Deployment: If the model’s performance is satisfactory, it can be used to make predictions on new, real-world data.
Supervised learning can be further categorized into two main types:
- Regression: In regression tasks, the goal is to predict a continuous numerical value as the output. For example, predicting house prices based on features like square footage, number of bedrooms, etc.
- Classification: In classification tasks, the goal is to assign input data to predefined classes or categories. For instance, classifying emails as “spam” or “not spam,” or identifying different types of animals based on their features.
Common algorithms used in supervised learning include linear regression (for regression), decision trees, support vector machines (SVM), k-nearest neighbors (KNN), and various types of neural networks.
Supervised learning is widely used in various real-world applications, including image recognition, natural language processing, medical diagnosis, financial forecasting, recommendation systems, and more. It relies on the availability of labeled training data, making it suitable for scenarios where the correct answers are known and can be used for training purposes.