That available data needs to be accessible and meaningful in order for the algorithm to extract information.
Let's consider a simple example. Imagine that we want to predict the market price of a house in a given city. We can think of many variables that would be predictors of the price of a house: the number of rooms or bathrooms, the neighborhood, the surface, the heating system, and so on. These variables are called features, attributes, or predictors. The value that we want to predict is called the outcome or the target.
If we want our predictions to be reliable, we need several features. Predicting the price of a house based on its surface alone would not be very efficient. Many other factors influence ...
