Re: Training Decision Tree

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Thank you :) What I mean is that I have two datasets onw to train the
decision tree and one for its evaluation. I want to use that specific
dataset to evaluate the decision tree and not one selected randomply form
SQL Server. I am using C4.5 algorithm and I have a training dataset ans a
test dataset. I want to use these two in SQL Server so I can compare the
C4.5 algorithm with the decision tree algorithm of SQL Server(using
entropy because C4.5 is an entropy based classifier).

Ok, so you have two datasets. You can use Lift Chart and / or Calssification
Matrix to measure the performance of the algorithm based on predictions on
test data set. Check the Lift Chart topic in Books OnLine if this suits you
(ms-help://MS.SQLCC.v9/MS.SQLSVR.v9.en/uas9/html/ab77eca1-bd48-4fef-b27f-ff5b648e0501.htm).
In MS Decision Trees, you can control couple of things through algorithm
parameters. You can use entropy as the score method of a split if you set
the
SCORE_METHOD parameter to 1. Check the Microsoft Decision Trees Algorithm
topic in Books OnLine
(ms-help://MS.SQLCC.v9/MS.SQLSVR.v9.en/uas9/html/95ffe66f-c261-4dc5-ad57-14d2d73205ff.htm).

--
Dejan Sarka
http://www.solidqualitylearning.com/blogs/


This is what i tried to do. First i created a mining structure using the test data set. I changed the parameters and i check the performance in lift chart.
Then i created a SSIS package with OLE DB Source the validation data set and i used it as an input to a data mining query. I run the package and browse the decision tree. The problem is that i got the same results as the first time (when i used the test data set), something that it is not possible.
What am i doing wrong?
.

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