What are dependent and independent variables?
In predictive modeling and statistics, dependent and independent variables are key concepts.
- Dependent Variable: The dependent variable is the main factor or outcome that you’re interested in predicting or understanding. It’s often denoted as “Y” in mathematical equations. In a study or experiment, the dependent variable is the variable that is measured or observed. For example, in a study looking at the effect of studying time on test scores, the test scores would be the dependent variable because they depend on the amount of time spent studying.
- Independent Variable: Independent variables are the factors or variables that are manipulated or controlled in a study. They are used to predict or explain changes in the dependent variable. Independent variables are often denoted as “X” in mathematical equations. In the study mentioned earlier, the independent variable would be the amount of time spent studying, as this is the variable that is being manipulated to see its effect on test scores.
What is Predictive Modeling ?
Predictive modelling is a process used in data science to create a mathematical model that predicts an outcome based on input data. It involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future or unknown events.
Table of Content
- What is predictive modelling?
- Importance of Predictive Modeling
- Applications of Predictive Modeling
- What are dependent and independent variables?
- How to select the Right model?
- What is training and testing data?
- Types of Predictive Models