Supervised Learning

What is supervised learning in AI?

Supervised learning is a type of machine learning where a model is trained on a labeled dataset, i.e., a dataset where the correct output is known for each input. The model learns a mapping from inputs to outputs and can then make predictions for new inputs.

How does supervised learning work?

Supervised learning works by minimizing a loss function, which measures the difference between the model's predictions and the true outputs. The model's parameters are adjusted iteratively to minimize this loss, using optimization algorithms like gradient descent.

Supervised learning can be used for both regression and classification tasks, depending on whether the output is continuous or categorical.

What are the applications and limitations of supervised learning?

Supervised learning has many applications, including image recognition, speech recognition, and predictive analytics. However, it also has limitations. It requires labeled training data, which can be expensive and time-consuming to collect. It also assumes that the future data will follow the same distribution as the training data, which may not always be the case.

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