What is Supervised learning?
Table of Content:
What is Supervised learning?
Supervised learning is a type of machine learning in which algorithms are trained on labeled data to make predictions or classifications. In supervised learning, the algorithm is given a set of input-output pairs, where the input data is labeled with the correct output. The algorithm then uses these labeled data to learn the relationship between the inputs and outputs and make predictions on new, unseen data.
Examples of supervised learning include:
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Regression: This is used to predict continuous values, such as stock prices or housing prices.
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Classification: This is used to predict categorical outcomes, such as whether a customer will buy a product or not.
Supervised learning algorithms use a variety of techniques, including linear regression, decision trees, and neural networks, to analyze and understand the relationships between the input data and the output predictions. The goal of supervised learning is to minimize the error between the predicted outputs and the true outputs in the training data.
Supervised learning is widely used in many applications, including image classification, speech recognition, and natural language processing. It is considered the simplest and most common form of machine learning, as it relies on labeled data and a clear understanding of the relationships between inputs and outputs.