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starling-lab / BoostSRL

BoostSRL: "Boosting for Statistical Relational Learning." A gradient-boosting based approach for learning different types of SRL models.
https://starling.utdallas.edu
GNU General Public License v3.0
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How can I understand the working of regression cases (continuous variables)? #42

Open CountingLogic opened 3 years ago

CountingLogic commented 3 years ago

As far as I understand, Boost SRL allows for only one query predicate. Now, in the regression case you discretise the predicates, which is clear for the non-query predicates, but once you discretise the query predicates, then you have many more queries, how is this handled ? Also, If I would like to interpret the decision trees as FOL rules, how can I do it in this case?

boost-starai commented 3 years ago

Thanks for the interest. The issue is that we do NOT discretize the target variable. So if you prefer to do regression, the gradients are now simply v-v’ where v is the true value and v’ is the current predicted value. This can again be found in several of our papers. This gradient is used for fitting the tree.

Again, we do NOT discretize the target. Only the non-query predicates are discretized.

Best, SN

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As far as I understand, Boost SRL allows for only one query predicate. Now, in the regression case you discretise the predicates, which is clear for the non-query predicates, but once you discretise the query predicates, then you have many more queries, how is this handled ? Also, If I would like to interpret the decision trees as FOL rules, how can I do it in this case?

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