Hi,
I wonder if you can please help me?
I am trying to perform some algorithms (Neural Network, SVM, Decision Tree and Random Forest) on my dataset.
What I am trying to see is how can I get Correlation Matrix, Confusion Matrix when I apply Model Comparison Node.
Basic Flow of my diagram is as follow
Import >>>> Stat Explorer >>>> Data Partition >>>> ANN, SVM, HP Forest, DT>>>> Control Point >>>> Model Comparison
Could you also please suggest any suitable node or critaria which may give me more results with these 4 algorithms in terms of best model comparison and results.
Regards
Hello,
Great Question.
A correlation matrix can be obtained using the variable clustering node.
Try attaching the model comparison node to the variable clustering node and then run the variable clustering node. From the results screen, click view > Model > Variable Correlation.
To surface the numeric values used to derive the graph, click on the Table shortcut button at the top right of the results screen.
An Event Classification Table is provided in the output window of the results in the Model Comparison node, which provides you the TN, FN, TP and FP counts.
With respect to the additional graphs or results, I find the model comparison node does a pretty good job. I really enjoy examining the Lift chart provided by the model comparison node because the lift charts gives us a graphical representation of each model's performance over the prior, that is a random guess.
I hope this helps!
Best,
Robert
Hello,
Great Question.
A correlation matrix can be obtained using the variable clustering node.
Try attaching the model comparison node to the variable clustering node and then run the variable clustering node. From the results screen, click view > Model > Variable Correlation.
To surface the numeric values used to derive the graph, click on the Table shortcut button at the top right of the results screen.
An Event Classification Table is provided in the output window of the results in the Model Comparison node, which provides you the TN, FN, TP and FP counts.
With respect to the additional graphs or results, I find the model comparison node does a pretty good job. I really enjoy examining the Lift chart provided by the model comparison node because the lift charts gives us a graphical representation of each model's performance over the prior, that is a random guess.
I hope this helps!
Best,
Robert
Thanks a lot Robert and sorry for late reply.
I have followed your suggestion and it works perfectly for me..
Thanks again
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