There are several models that can be implemented in the form of algorithms (mathematical and computerized) exist in supervised learning and differ not only in how they approach training the data, but also in the type of labels they predict (continuous values, classes, etc.).
Linear regression is one of the most popular supervised learning techniques for predicting continuous values.
For example, this model can be used to predict the price of a home by taking into account its size, number of bedrooms, and location.
This model is very good at capturing the linear relationship between the explanatory variables and the explained variable, especially thanks to its variant (the version with regularization to avoid overfitting), but the It still doesn't work if there is a more complex relationship between them than simple linearity. .
In other monitored tasks, classification etc. You can find several models, including models based on decision trees (such as RandomForest) and variants of regression such as SVM Support Vector Machine Logistic Regression.
supervised learning Although these algorithms are at the cutting edge of classical machine learning, you are not limited to them.
In fact, deep learning based on deep neural networks provides better results for supervised learning in the context of complex problems such as classification of unstructured data (images, audio, video) as well as for classical machine learning problems. It is also widely used to obtain
