as the title says, any chance any1 can help?
Use the process Credit_Risk_Modeling_Course.rmp.
Read the data-set: Credit_Risk_Modelling.csv
Run the process.
a. (2%) How are the train and test divided in this process?
b. (4%) The “Improved Neural Net” is one of the outputs. Explain the graph
by applying the explanation from class (3-4 sentences.)
c. (4%) Compare the confusion matrices in the results using the recall and
performance rates. (3-4 sentences.)
For your own knowledge - review the output with the LR coefficients.
d. (10%) Explain the table NN – Opt before making changes.
Then, add another optimization parameter for the NN optimization (see
the tutorial slides), such that there will be 20 rows in the new NN-Opt
table. Make the optimized NN model win in the confusion matrix
comparison. Explain.
e. (12%) Add another model from a different family from the following: KNN,
DT, RF.
i. (6%) Add the model as a separate model in a similar manner of
“NN-CV-No Opt” (you can copy-paste and edit the actual model
that is used inside the CV. It is recommend to also rename the
varied modules.)
Analyze the results.
ii. (6%) Use the Optimization model. Add the model of your choice to
the current two competing models.
