feat: log regression metrics as parameters in Training class

- Added a method to persist computed regression metrics (MSE, MAE, R²) as MLflow parameters during model training, enhancing model evaluation and tracking.
- Updated the Training class to log the equation path if available, improving artifact management.

tests/conftest.py

@@ -17,6 +17,9 @@ def _make_dummy(name: str) -> type:
 
 def _stub_evidently() -> None:
     """Minimal Evidently API surface required to import `model_manager.sientia.reports`."""
+    ev = ModuleType('evidently')
+    sys.modules['evidently'] = ev
+
     mp = ModuleType('evidently.metric_preset')
     mp.DataDriftPreset = _make_dummy('DataDriftPreset')
     sys.modules['evidently.metric_preset'] = mp
@@ -51,6 +54,13 @@ def _stub_evidently() -> None:
     opt.ColorOptions = _make_dummy('ColorOptions')
     sys.modules['evidently.options'] = opt
 
+    pipeline = ModuleType('evidently.pipeline')
+    sys.modules['evidently.pipeline'] = pipeline
+
+    colmap = ModuleType('evidently.pipeline.column_mapping')
+    colmap.ColumnMapping = _make_dummy('ColumnMapping')
+    sys.modules['evidently.pipeline.column_mapping'] = colmap
+
     rep = ModuleType('evidently.report')
     rep.Report = _make_dummy('Report')
     sys.modules['evidently.report'] = rep