Understanding supervised machine learning
In supervised machine learning, the data used for training contains known past outcomes, referred to as dependent or target variable(s). These are the variables you want your machine learning (ML) model to predict. The ML algorithm learns from the data using all other variables, known as independent or predictor variables, to determine how they are used to estimate the target variable. For example, the target variable is the house price in the house pricing prediction problem. The other variables, such as the number of bedrooms, number of bathrooms, total square footage, and city, are the independent variables used to train the model. You can think of the ML model as a mathematical model for making predictions on unobserved outcomes.
On the other hand, in unsupervised machine learning, the data contains no labels or outcomes to train on (unknown or unobserved). Unsupervised algorithms are used to find patterns in the data, such as the case...
