Supervised learning algorithms are a type of machine learning algorithm that require labeled data for training. In supervised learning, we provide the algorithm with input data and the corresponding output labels, and the algorithm learns to map the input data to the correct output labels. Once the algorithm has been trained on a sufficiently large and diverse dataset, it can be used to make predictions or decisions on new, unseen data.
There are several types of supervised learning algorithms, including classification and regression.
Classification algorithms are used when the output labels are discrete or categorical, such as whether an email is spam or not. The algorithm learns to classify new input data into one of the predefined categories based on the features of the input data.
Regression algorithms are used when the output labels are continuous, such as predicting the price of a house based on its size and location. The algorithm learns to map the input data to a continuous output value, typically using a mathematical function.
Supervised learning algorithms require a large and diverse labeled dataset for training, and the quality of the predictions or decisions made by the algorithm is highly dependent on the quality and quantity of the labeled data. Supervised learning algorithms are widely used in many applications, such as image and speech recognition, sentiment analysis, and credit scoring.