Supervised learning is a popular approach in machine learning where a model learns from labeled training data. It involves training a model on a set of input-output pairs, also known as labeled examples. The model then uses this training data to make predictions on new, unseen data. The objective is to minimize the difference between the predicted outputs and the actual labels. An algorithm called the loss function calculates this difference and determines how well the model is performing. This calculated loss is used to update the model’s parameters to improve its accuracy. Supervised learning can be further classified into two types: regression, where the output is a continuous value, and classification, where the output is a discrete value. Regression tasks include predicting house prices, while classification tasks involve classifying emails as spam or non-spam. Supervised learning is widely used in various fields such as image recognition, natural language processing, and fraud detection.
No.
No shit Sherlock. You think your so smart for figuring that out? Get F’ed,