chore: Update MLXFilter model deployment and logging with new training results and ONNX file management

- Added new training log entries for lgbm backend with AUC, precision, and recall metrics.
- Enhanced deploy_model.sh to manage ONNX and lgbm model files based on the selected backend.
- Adjusted output shape in mlx_filter.py for ONNX export to support dynamic batch sizes.

src/mlx_filter.py

@@ -84,7 +84,7 @@ def _export_onnx(
         nodes,
         "mlx_filter",
         inputs=[helper.make_tensor_value_info("X", TensorProto.FLOAT, [None, input_dim])],
-        outputs=[helper.make_tensor_value_info("proba", TensorProto.FLOAT, [None])],
+        outputs=[helper.make_tensor_value_info("proba", TensorProto.FLOAT, [-1])],
         initializer=initializers,
     )
     model_proto = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 17)])