VLSI Simulation with XGBoost Model

I’m modeling a VLSI circuit’s behaviour entirely through simulation and want to drive the process with an XGBoost Multi-Output Regressor. The goal is to replace portions of a traditional SPICE run with a machine-learning proxy that can predict multiple analog metrics at once (delay, power, noise, etc.) from layout-level or netlist-level features.

Key details
• Scope: pure simulation workflow; no verification or post-layout optimisation tasks are needed.
• Model choice: XGBoost Multi-Output Regressor is fixed.
• Critical tweak: I need a custom number of estimators rather than the default setting; help me decide and implement the optimal value through systematic tuning.

What I expect from you
1. Clean, reproducible Python (preferably a Jupyter Notebook) that:
– Pre-processes my CSV/netlist-derived dataset,
– Trains and cross-validates the XGBoost model,
– Outputs predictions for all target metrics in a form that can slot back into my current simulation flow.
2. Brief documentation describing feature engineering choices, chosen estimator count, and any other hyper-parameters.
3. A short note on how to extend the model to new process corners or larger datasets.

Tools on my side
I’m already set up with Python 3.10, scikit-learn, and xgboost on an Ubuntu workstation, so please align with that stack.

Deliver the notebook, a requirements.txt (if extra libraries are needed), and a sample run showing the model converging and producing multi-output predictions. If everything runs smoothly on my end and the metrics meet or beat my current SPICE run time/accuracy trade-off, the job is finished.