feat(linear): Add ensemble tree model

[shenkha]

What does this PR do?

This pull request introduces a major enhancement to the linear tree-based models by implementing Ensemble Tree Models, which improves prediction accuracy and robustness over a single tree model.

Key Changes:

1. Ensemble of Trees

• A new EnsembleTreeModel class in libmultilabel/linear/tree.py now manages multiple tree models.
• The train_ensemble_tree function handles the training of n separate tree models, each with a different random seed for diversity.
• The ensemble's final predictions are an average of the scores from each tree, providing a more stable and accurate result.
• This functionality is exposed via a new CLI argument --tree_ensemble_models in main.py and integrated into linear_trainer.py.

Example usage:

python main.py --training_file data/eurlex_raw_texts_train.txt \
                --test_file data/eurlex_raw_texts_test.txt \
                --linear \
                --linear_technique tree \
                --tree_ensemble_models 3

Test CLI & API (bash tests/autotest.sh)

Test APIs used by main.py.

• Test Pass
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• Not Applicable (i.e., the PR does not include API changes.)

Check API Document

If any new APIs are added, please check if the description of the APIs is added to API document.

• API document is updated (linear, nn)
• Not Applicable (i.e., the PR does not include API changes.)

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.

[Eleven1Liu]

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