Explanatory Variables, also known as independent variables or predictors, refer to the variables in a statistical model that are used to predict or explain the outcome variable. These variables can range from simple things like age and gender to more complex variables such as spending habits, location, and more.
In machine learning, Explanatory Variables are used to identify the variables that have a significant impact on the model's outcome. These variables are used to create a model that can predict future outcomes or behaviors. The model is then trained using a dataset that contains both the outcome variable (what the model wants to predict) and the explanatory variables (variables that may have a relationship with the outcome). Once the model is trained, it can make predictions based on new data that contains only explanatory variables. This process is often referred to as supervised learning.
Explanatory Variables allow businesses to make predictions and decisions based on data. By understanding which variables are most influential in a certain outcome, businesses can tailor their strategies and make data-driven decisions that are more likely to result in success. This helps in optimizing marketing campaigns, forecasting sales, and adjusting business strategies for better performance.
Related technologies and terms include:
Principal component analysis
H2O’s machine learning platform is an excellent choice for working with Explanatory Variables. With its powerful data visualization and data exploration tools, businesses can quickly identify the most important explanatory variables and use them to train models that accurately predict customer behavior. Additionally, H2O’s platform includes advanced algorithms and optimization techniques that enable businesses to achieve the best possible results.