Independent Variables
In Data Science, independent variables are the input variables that are used to predict the outcome of a dependent variable. They are also known as predictor variables or features. Independent variables can be either categorical or continuous. Categorical independent variables are those that have a limited number of possible values, such as gender or color. Continuous independent variables are those that can take on any value within a range, such as age or temperature. In machine learning, the selection of independent variables is crucial for building accurate predictive models. The process of selecting the most relevant independent variables is called feature selection. It involves identifying the variables that have the strongest relationship with the dependent variable and removing any redundant or irrelevant variables.
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