Learning structure
Learning structure
Hello,
I'm a Genie new user. I searched some documentation about genie and how use it but I dont understand the option of the different algorithms as in greedy thick thinning how can I choose K2 or BDeu and what is the meaning of Network weight. I didn't find documentation about greedy thick thinning and essential graph search.
Moreover, I want to know how choose the best network (can I find the score of a network ?).
Is it possible to do supervised analyse without naive bayes method ? After learning we can specify a target node (when we use PC for example) but can we trace the ROC curve or can we export the probability of target state with the network ?
Thanks
Lytia
Sorry for my english !
I'm a Genie new user. I searched some documentation about genie and how use it but I dont understand the option of the different algorithms as in greedy thick thinning how can I choose K2 or BDeu and what is the meaning of Network weight. I didn't find documentation about greedy thick thinning and essential graph search.
Moreover, I want to know how choose the best network (can I find the score of a network ?).
Is it possible to do supervised analyse without naive bayes method ? After learning we can specify a target node (when we use PC for example) but can we trace the ROC curve or can we export the probability of target state with the network ?
Thanks
Lytia
Sorry for my english !
Please refer to Heckerman's "A Tutorial on Learning With Bayesian Networks" for an explanation of GreedyThickThinning. Essential graph search starts from a graph obtained by applying PC and then continues with a GreedyThickThinning search (and it also does multiple restarts). The learning algorithms automatically select the best networks based on their scores (except PC which doesn't use a score).
It's possible to use any Bayesian network for supervised analysis and it's not necessary to use naive Bayes. GeNIe has target nodes and also a diagnosis module that may be useful in your case.
It's possible to use any Bayesian network for supervised analysis and it's not necessary to use naive Bayes. GeNIe has target nodes and also a diagnosis module that may be useful in your case.
Best model
When I learn structure of a network with greedy thick thinning method and then with essential graph search with the same data file. How can I choose the best model ? Can I show the score of each model ?
Moreover, if I do supervised analysis, can I export a data file with a new variable composed by the probability of the target learned by the network ?
Thanks
Lytia
Moreover, if I do supervised analysis, can I export a data file with a new variable composed by the probability of the target learned by the network ?
Thanks
Lytia
Re: Best model
At the moment you cannot see the scores of the network, but it's probably a good idea to show them. In your case, I think it was empirically shown that the essential graph search leads to better results. See here: http://www.pitt.edu/~druzdzel/abstracts/uai99.htmllytia85 wrote:When I learn structure of a network with greedy thick thinning method and then with essential graph search with the same data file. How can I choose the best model ? Can I show the score of each model ?
I don't understand what you want to do. Saving a learned network is possible, of course, but what else do you want to do?lytia85 wrote: Moreover, if I do supervised analysis, can I export a data file with a new variable composed by the probability of the target learned by the network ?
best classifier
I want to determinate the prediction value of the target node. So I want to calculate matrix confusion or trace ROC curve in order to define the best model of prediction. For this, I must export the probability of the target obtained by the network.
Thanks
Lytia
Thanks
Lytia
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Re:
When i Learning structure with GreedyThickThinning (for example) i actually get Bayesian network when the links between variables explained as correlation / association ?mark wrote:Please refer to Heckerman's "A Tutorial on Learning With Bayesian Networks" for an explanation of GreedyThickThinning. Essential graph search starts from a graph obtained by applying PC and then continues with a GreedyThickThinning search (and it also does multiple restarts). The learning algorithms automatically select the best networks based on their scores (except PC which doesn't use a score).
It's possible to use any Bayesian network for supervised analysis and it's not necessary to use naive Bayes. GeNIe has target nodes and also a diagnosis module that may be useful in your case.
The learning is Unsupervised?
Thakns,
Boris
Re: Learning structure
In a nutshell, the arcs are causal, unless an arc can be reversed without changing the set of conditional independencies that hold for a given graph. The learning is unsupervised.
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Re: Learning structure
If the arc can be reversed without any changing it will be deleted or appear as correlated?mark wrote:In a nutshell, the arcs are causal, unless an arc can be reversed without changing the set of conditional independencies that hold for a given graph. The learning is unsupervised.
Another question:
The K2 and the BDeu are score metrics for the greedy algorithm?
What is the meaning of the the BDeu's weights ?
Thanks,
Boris
Re: Learning structure
If the arc can be reversed it means there is a direct correlation between the variables, but the causal relationship cannot be determined. For example, if you have two discrete variables that are correlated it is not possible to learn from data which way the causal connection goes. K2 and BDeu are prior distributions over parameters used in the score metric.
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Re: Learning structure
Are these kind of arcs presented ?mark wrote: If the arc can be reversed it means there is a direct correlation between the variables, but the causal relationship cannot be determined
What the BDeu's weights meaning?mark wrote: K2 and BDeu are prior distributions over parameters used in the score metric
Thanks,
Boris
Re: Learning structure
Yes, these arcs will be part of the network that's outputted. The BDeu weight expresses the strength of a prior belief in the uniformity of the conditional distributions in the network.
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Re: Learning structure
Can you please indicate the run time for the greedy algorithm when using the K2 and BDeu ?mark wrote: K2 and BDeu are prior distributions over parameters used in the score metric.
Thanks,
Boris
Re: Learning structure
Do you mean to ask if there is a difference? In general, the runtime depends strongly on the connectivity of the graph you are trying to learn (i.e., the number of conditional dependencies in the data).
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Re: Learning structure
I actually intended to ask the run time for the greedy algorithm as function of nodes .mark wrote:Do you mean to ask if there is a difference? In general, the runtime depends strongly on the connectivity of the graph you are trying to learn (i.e., the number of conditional dependencies in the data).
Separate when using the K2 and separate for the BDeu.
Thank you very much,
Boris