# Statistical Learning

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**Input Variable:**

An input variable is data that is fed into an algorithm. It is what is used to predict an output variable. Columns in a data set that are inputs are known as input variables. In modeling, you usually have more than one input variable. A group of input variables is known as the input vector. The input variable is also known as the independent variable. An easy way to remember this is in the phrasing of the prediction problem. The output is dependent on input or independent variables. Dependent variables and independent variables are described using shorthand in equations:

Input variables: X

Output variables: Y

When there are many input variables, they are referenced with an integer to show order. The input vector would read X1, X2, X3, and Xn for the first columns in the data set. When modeling a problem, it is common to refer to input attributes and output attributes. The statistical terms of input and output variables are denoted as X and Y. (Brownlee, 2016)

**Output Variable:**

Brownlee, J. (2016, March 9). *Machine Learning Terminology from Statistics and Computer Science*. Retrieved from machinelearningmastery: https://machinelearningmastery.com/data-terminology-in-machine-learning/