@Eric Hanson Thank you.
I was caught up in the preprocessing steps.
The main ones I encountered that are
essential for working with Lighthouse are:
actual_test = parse.(Int64, (format.(test2[!,:Ninjas], precision = 0)))
predicted_test = parse.(Int64, (format.(ypredicted_test, precision = 0)))
size(actual_test)
size(predicted_test)
This step is also a QA to make sure your
confusion matrix is mapped 1:1
confusmat(56, actual_test, predicted_test)
Then as needed, you can apply Lighthouse
for items such as:
Hope this helps someone out there.
:koala:
@Eric Hanson
Do you know how to apply
binary_statistics (C, precision)?
and reference it in the println()
I have added the function HERE
And added the binary_statistics function found right below the function
found at the link above.
Do I need to convert the matrix to a dataframe:
Bucket = DataFrame(binary_statistic(C))
Then reference the index as:
println("The Precision Score is", mean(Bucket[!, :precision]))
Yes, I have imputed the values. Might there be a
more elegant approach?
Thank you,
Last updated: Nov 06 2024 at 04:40 UTC