Expose classes_/predict_proba on calibrated classifiers for global explainers

[Creylay]

Summary

LinearSVCClassifier and SGDClassifier calibrate probabilities via an internal CalibratedClassifierCV (self._calibrated) instead of fitting themselves, so they never exposed classes_ or predict_proba on the wrapper instance. sklearn's inspection utilities (permutation_importance, partial_dependence) read estimator.classes_ unconditionally on any object tagged as a classifier, so running a global explainer (Permutation Feature Importance, Partial Dependence) on either model crashed with AttributeError: '...Classifier' object has no attribute 'classes_'. Local explainers were unaffected since they call model.predict() directly, bypassing that machinery.

Fix proxies classes_ and predict_proba from the internal calibrated model onto self, so both models now work correctly with all explainers instead of just being blocked from using them.

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Type of Change

Check all that apply like this [x]:

• Backend change
• Frontend change
• CI / Workflow change
• Build / Packaging change
• Bug fix
• Documentation

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Changes (by file)

• DashAI/back/models/scikit_learn/linear_svc_classifier.py: added classes_ property and predict_proba method that delegate to the internal CalibratedClassifierCV.
• DashAI/back/models/scikit_learn/sgd_classifier.py: same fix, same root cause.
• tests/back/explainers/test_explainers.py: added regression tests running Permutation Feature Importance and Partial Dependence against both calibrated classifiers.

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Testing (optional)

To verify manually:

1. Train a Linear SVC or SGD Classifier model on any tabular classification dataset.
2. Go to Explainability → add a global explainer (Permutation Feature Importance or Partial Dependence) and run it.
3. Before this fix, it would fail with AttributeError: '...Classifier' object has no attribute 'classes_'. It should now complete successfully.

[cristian-tamblay]

This was already fixed by #812 . Closing this PR.