Currently all of the metrics computed are independent of a target variable or column, but if lens.summarise took the name of a column as the target variable, the output of some metrics could be more interpretable even if the target variable is not used in any kind of predictive modelling.
A good example of this could be PCA (see #14), which could plot the different categories of the target variables in different colours for 2D plots of the data transformed into the principal components. This would give a good idea of whether the target variable can be easily inferred from the available data.
Currently all of the metrics computed are independent of a target variable or column, but if
lens.summarisetook the name of a column as the target variable, the output of some metrics could be more interpretable even if the target variable is not used in any kind of predictive modelling.A good example of this could be PCA (see #14), which could plot the different categories of the target variables in different colours for 2D plots of the data transformed into the principal components. This would give a good idea of whether the target variable can be easily inferred from the available data.