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/utils/models/test_train_model_params.py

@@ -5,7 +5,10 @@ from unittest.mock import patch
 
 import pytest
 
-from model_manager.utils.models.train_model_params import TrainModelParams
+from model_manager.utils.models.train_model_params import (
+    TrainModelParams,
+    validate_frontend_date_format,
+)
 
 
 @pytest.fixture
@@ -262,3 +265,11 @@ def test_validate_model_param_all_schema_branches(valid_train_params_dict):
     p.model_kwargs = {}
     p.opt_params = {}
     p.validate_business_rules()
+
+
+def test_validate_frontend_date_format_whitespace_returns():
+    validate_frontend_date_format('   ')
+
+
+def test_validate_frontend_date_format_valid_returns():
+    validate_frontend_date_format('dd/MM/yyyy HH:mm:ss')