Linear modeling methods have been expanded with two new functions — regression decision tree and linear regression.
Regression decision tree allows you to build models in the form of a hierarchical structure with rules of the type “If …, then …”, which helps to identify complex dependencies between variables and make decisions based on dividing data into subgroups.
Linear regression identifies linear dependencies between independent and dependent variables, allowing you to accurately assess the influence of various factors on the result. This method helps build forecasts and is used to predict numerical values.
Both tools significantly expand the capabilities of data analysis, allowing you to better understand the relationships and more accurately predict results.
Use the updated functionality right now at the link.
