Add normalize=False parameter to MLXFilter.fit() so external callers
can skip internal normalization. Remove the external normalization +
manual _mean/_std reset hack from walk_forward_auc() in train_mlx_model.py.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- 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.
- Updated `train_model.py` and `train_mlx_model.py` to include a time weight decay parameter for improved sample weighting during training.
- Modified dataset generation to incorporate sample weights based on time decay, enhancing model performance.
- Adjusted deployment scripts to support new backend options and improved error handling for model file transfers.
- Added new entries to the training log for better tracking of model performance metrics over time.
- Included ONNX model export functionality in the MLX filter for compatibility with Linux servers.
