Fix two issues in linear tree-based model: #1
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1. We should not filter any data instance in the root.
(In the original code, we do not include the instances with zero label in the training.)
2. The outputs of a tree-based model should be in the range [0, 1]^{# of label}, which is corresponding to the probability estimates.
Moreover, if we want to use sparse matrix to store the prediction values of a tree model, the value ``-inf'' will be a trouble issue.
Eleven1Liu
approved these changes
Dec 12, 2024
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We should not filter any data instance in the root. (In the original code, we do not include the instances with zero label in the training.)
The outputs of a tree-based model should be in the range [0, 1]^{# of label}, which is corresponding to the probability estimates. Moreover, if we want to use sparse matrix to store the prediction values of a tree model, the value ``-inf'' will be a trouble issue. (This one is fixed by PR keep the output of linear tree-based model as probability estimates #2)
What does this PR do?
add data rule for root in tree-based model
Test CLI & API (
bash tests/autotest.sh)Test APIs used by main.py.
Check API Document
If any new APIs are added, please check if the description of the APIs is added to API document.
Test quickstart & API (
bash tests/docs/test_changed_document.sh)If any APIs in quickstarts or tutorials are modified, please run this test to check if the current examples can run correctly after the modified APIs are released.