Can we have two target variables in machine learning?
Multi Target Regression Machine Learning classifiers usually support a single target variable. For classification models, a problem with multiple target variables is called multi-label classification.
Can a model have more than one dependent variable?
We present a more general method which allows models to be constructed with multiple variables on both sides of an equation and which can be computed easily by using a spreadsheet program. Single-equation models involving multiple dependent as well as multiple independent variables are much rarer in the literature.
What do you call problem with multiple target variables?
F o r classification models, a problem with multiple target variables is called multi-label classification. In the realm of regression models, as a beginner, I found the nomenclature a bit confusing.
How can I check the correlation between features and target variable?
The following correlation output should list all the variables and their correlations to the target variable. The negative correlations mean that as the target variable decreases in value, the feature variable increases in value. (Linearly)
When to change the Order of target variables?
For every value on the explanatory variable, you have a coordinate measurement on the target variable. If you don’t change the order of either, then the relation remains intact. Right? Thanks for contributing an answer to Cross Validated!
Can a regression model support multiple target variables?
Multi Target Regression Machine Learning classifiers usually support a single target variable. In the case of regression models, the target is real valued, whereas in a classification model, the target is binary or multivalued. F o r classification models, a problem with multiple target variables is called multi-label classification.