Supervised learning is a type of machine learning where a model is trained on a labeled dataset, which means that the input data has corresponding output or target values. The goal of supervised learning is to train a model that can accurately predict the output or target values for new, unseen input data.
There are two main types of supervised learning: classification and regression.
Classification is used for tasks where the output or target variable is categorical, such as identifying the type of object in an image. For example, a model could be trained to distinguish between images of cats and dogs. After the training, the model can be given a new image, and it will predict whether the image contains a cat or a dog.
Regression, on the other hand, is used for tasks where the output or target variable is continuous, such as predicting the price of a house based on its characteristics. In this case, the model would be trained on a dataset of houses that includes information such as square footage, number of bedrooms, and location, along with the corresponding sale price. After the training, the model can be given information about a new house, and it will predict its sale price.
Supervised learning algorithms include linear regression, logistic regression, k-nearest neighbor, decision trees, and many others. These algorithms are widely used in many industries such as finance, healthcare, marketing, and transportation.
In summary, Supervised learning is a way of training a model to predict the output or target values for new, unseen input data using the labeled datasets, it’s widely used for both classification and regression tasks.