[SOLVED] Bayes NET
Hello,
I would do the following:
Input: a,b,c,d,z (random variable/ table columns)
1. Create a decision tree which shows the influences on z
Example: http://monkpublic.library.illinois.edu/monkmiddleware/public/analytics/decision_tree.png
2. Create a probability table for z.
Example:
z : P(z|a,b,c,d)
z_1 : 25% with a=1,b=1;c=0; d=true
z_2 : 12.5% with a=1,b=1;c=0;d=false
etc.
My problems with the bayes.NET are
a) The probability table is very large, can I write directly for z in a file (Bayes.NET gives me unfortunately for each variable, a table...).
b) I can learn the tree-structure restricted to z?
mfg student
I would do the following:
Input: a,b,c,d,z (random variable/ table columns)
1. Create a decision tree which shows the influences on z
Example: http://monkpublic.library.illinois.edu/monkmiddleware/public/analytics/decision_tree.png
2. Create a probability table for z.
Example:
z : P(z|a,b,c,d)
z_1 : 25% with a=1,b=1;c=0; d=true
z_2 : 12.5% with a=1,b=1;c=0;d=false
etc.
My problems with the bayes.NET are
a) The probability table is very large, can I write directly for z in a file (Bayes.NET gives me unfortunately for each variable, a table...).
b) I can learn the tree-structure restricted to z?
mfg student
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Thank you. but I see just the real problem: I have an out-of-memory-exeption.
My questions are:
1. Can you tell me how I can summarize table row?
Example:
[id1,ja,100,50]
+
[id1,ja,50,12]
=
[id1,ja,150,62]
2. I would like a "loop value" loop which I only create the line which outputs in a special column "y" has the value "1".
"loop value" with "attribute=y" and "iteration macro =1" gives me the columns with 0 and 1 => out of memory
mfg
student
My questions are:
1. Can you tell me how I can summarize table row?
Example:
[id1,ja,100,50]
+
[id1,ja,50,12]
=
[id1,ja,150,62]
2. I would like a "loop value" loop which I only create the line which outputs in a special column "y" has the value "1".
"loop value" with "attribute=y" and "iteration macro =1" gives me the columns with 0 and 1 => out of memory
mfg
student
hi,
the functions "aggregate" and "filterExamples" solve my problem.
how I can save one attributs probability table (from bayesNet) in a file?
the functions "aggregate" and "filterExamples" solve my problem.
how I can save one attributs probability table (from bayesNet) in a file?
Which bayes net operator exactly are you using?
hallo,
weka.classifiers.bayes.net.search.local.K2 -- -P 22 -S BAYES
and
weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5
My problem is that the probability tables for some nodes are not displayed. (it would probably be too big, I have 13 gb RaM)
weka.classifiers.bayes.net.search.local.K2 -- -P 22 -S BAYES
and
weka.classifiers.bayes.net.estimate.SimpleEstimator -- -A 0.5
My problem is that the probability tables for some nodes are not displayed. (it would probably be too big, I have 13 gb RaM)
So obviously you are using the W-BayesNet operator.
How many examples are you using? Can you please post your process setup? Or even better, can you create a process which demonstrates the problem which uses only the samples data set or the Generate Data operators, such that we are able to reproduce the problem?
Furthermore, let me ask which version of RapidMiner and the Weka extension are you using? The latest version of RapidMiner is 5.3.7, of the Weka extension 5.3.1. If you are using lower versions, please upgrade.
Best regards,
Marius
How many examples are you using? Can you please post your process setup? Or even better, can you create a process which demonstrates the problem which uses only the samples data set or the Generate Data operators, such that we are able to reproduce the problem?
Furthermore, let me ask which version of RapidMiner and the Weka extension are you using? The latest version of RapidMiner is 5.3.7, of the Weka extension 5.3.1. If you are using lower versions, please upgrade.
Best regards,
Marius
thanks. I have found my error.
concerning the tree structure, did you watch the introductory videos on our website? In general, you can use one of the Decision Tree operators to create tree structures.
Best regards,
Marius