In machine learning, you'll often hear the terms features, predictors, and dependent variables. They are all one and the same. They all refer to the variables that are used to predict an outcome. In our previous example of cars, the variables cyl (Cylinder), hp (Horsepower), wt (Weight), and gear (Gear) are the predictors and mpg (Miles Per Gallon) is the outcome.

In simpler terms, taking the example of a spreadsheet, the names of the columns are, in essence, known as features, predictors, and dependent variables. As an example, if we were given a dataset of toll booth charges and were tasked with predicting the amount charged based on the time of day and other factors, a hypothetical example could be as follows:

In this spreadsheet, the columns date, time, agency, type, prepaid, and rate are the features or predictors, whereas, the column amount is our outcome or dependent variable (what we are predicting).

The value of amount depends on the value of the other variables (which are thus known as independent variables).

Simple equations also reflect the obvious distinction, for example, in an equation, y = a + b + c, the left hand side (LHS) is the dependent/outcome variable and a, b and c are the features/predictors.

In summary: