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What is Supervised Learning?
Supervised learning is a machine learning technique in which an algorithm learns from labeled training data to make predictions or decisions. It is called "supervised" because the training data includes both input features and corresponding target labels, providing a supervisor or teacher to guide the learning process.
In supervised learning, the algorithm aims to learn a mapping function that can predict the correct output or label given an input. The process involves two main components: the training phase and the prediction phase. During the training phase, the algorithm analyzes the labeled data to learn patterns, relationships, and dependencies between the input features and the corresponding output labels. It adjusts its internal parameters or model based on the training examples to minimize the prediction errors.The prediction phase involves applying the learned model to new, unseen data to make predictions or classify new instances based on their input features. The algorithm uses the acquired knowledge from the training phase to generalize and make accurate predictions on the unseen data.
Supervised learning has a wide range of applications, including image recognition, speech recognition, natural language processing, fraud detection, medical diagnosis, and many others. It is a powerful and widely used approach in machine learning, as it leverages labeled data to train accurate models that can make predictions on new, unseen data. However, the availability of high-quality labeled data is crucial for the success of supervised learning algorithms, as the accuracy of the learned model heavily depends on the quality and representativeness of the training examples.
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