In this section I would like to briefly discuss the difference between independent and dependant variables in machine learning.
Using a very simple example let’s apply the definition that independent variables (also referred to as Features) are the input for a process that is being analyzed and dependent variables are the output of the process. So, if we have a small data frame that tracked distance and time we would say time is our independent variable and distance is the dependent variable because the distance traveled depends on how much time has passed.
For a machine learning data frame I’m going to use our previous example of dynamic learning in the classroom. Our independent variables are school year and semester, the professor, course, course title, and dynamic learning. The dependent variable being average grade. In this example we have multiple independent variables and one dependent, but depending on the data your working with you can also have multiple of both.
