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You are correct, because your model is not saved as a Model Object (an output of the Alteryx Predictive Tools) you will not be able to use the Lift Chart or Model Comparison Tools, as they require a Model Object as an input.
I also believe you identified the two best courses of action for calculating the Gini Coefficient in Alteryx without a Model object; either from the bottom up, generating a Lorenz Curve, or creating custom R code.
I personally think that the easier of the two would be to create the custom R code. The Gini() function comes from the MLmetrics R package, which is included with the Alteryx Predictive Tools installation. This means you will not need to install any new packages to access the function, if you have the Predictive Tools installed it is already on your machine.
The arguments of the function also seem to be relatively straightforward. You would need to bring in a data frame with two fields, one with the predicted probabilities output by your model, and the other with 1s and 0s, 1's indicating the models predicted correctly , 0's indicating false. You will want your data types to be numeric
The code would look something like this - all you need to do is read in the data with the read.Alteryx function, call the Gini function on your data (COLUMNAME and COLNAME2 should be replaced with the correct field names) and then write out the output with the write.Alteryx function.
#read in your dataframe
data <- read.Alteryx("#1", mode="data.frame")
#use Gini function
gini.index <- MLmetrics::Gini(y_pred = data$COLUMNNAME, y_true = data$COLNAME2)
#write out Gini index
Does this all make sense? Please let me know if you have any questions!