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Perfect, now I see the full details.

Andrew says:

• He’s testing TensorFlow + XGBoost models for ASW 5d prediction.
• His results are decent but not great (RMSE ~1.5, MAPE 4.5).
• He's facing overfitting issues.
• He compliments your Random Forest approach (non-ML ensemble) saying it looks excellent.
• He also likes your regime shift model ("Very cool").

Thanks to this, here’s an even better, polished reply you can send:

---

Subject: RE: Key Information: Regime Change Detected for ASW5Y

Hi Andrew,

Thank you for your email, and I’m really glad to hear you’re getting to spend such precious time with your daughter — enjoy every moment!

It’s great to hear that you’re experimenting with TensorFlow and XGBoost for the ASW5Y prediction. Your results are actually very solid given the complexity of the signal — although I completely understand the challenge with overfitting. It’s definitely a tricky balance when using more flexible models, especially on financial time series with a relatively low signal-to-noise ratio.

I’m really glad you found the Random Forest approach useful. It was indeed designed with simplicity and robustness in mind, especially to avoid overfitting and ensure interpretability for the front office.

On the regime shift side, I'm continuing to refine it. I also think combining the regime signals with the prediction models could be a very powerful angle for future iterations. Would be great to brainstorm together when you're back!

Looking forward to reconnecting soon. Wishing you a wonderful continuation of your paternity leave!

Best regards,
Noussaire

---

Would you like also a second even slightly more technical version, where you could propose that when he’s back, you compare TensorFlow/XGBoost/Random Forest under regime-separated datasets? (If you want to show you're already thinking next steps.)
Just tell me!

### Slide: Recursive Feature Selection in 

Random Forest

**Title:** Recursive Feature Selection Technique

**Overview:**

- Recursive Feature Selection (RFS) is used to improve model accuracy by selecting the most relevant features.

**Key Concepts:**

1. **Feature Importance:**

   - RFS ranks features based on their importance to the model's predictions.

   - Helps in identifying and retaining the most impactful features.

2. **Iterative Process:**

   - Features are recursively removed, and the model is re-evaluated to determine the optimal subset of features.

   - This process continues until the best performing set of features is found.

3. **Model Simplification:**

   - By eliminating irrelevant or less important features, RFS simplifies the model.

   - Reduces overfitting and improves model generalizability.

**Impact on Backtest Performance:**

- **Before RFS:**

  - Absolute Average Prediction Error: 1.51 bp

- **After RFS:**

  - Absolute Average Prediction Error: 1.19 bp

  - This improvement demonstrates the effectiveness of RFS in enhancing model accuracy by focusing on the most relevant features.

**Advantages:**

- **Improved Accuracy:** By selecting the most important features, the model's prediction accuracy improves.

- **Reduced Complexity:** Simplifies the model by removing unnecessary features.

- **Enhanced Generalization:** Leads to better performance on unseen data.

**Visual Aid:**

- (Include a flowchart or diagram illustrating the recursive feature selection process, showing feature ranking, iterative removal, and model re-evaluation.)

---

**Visual Elements:**

- **Title:** "Recursive Feature Selection Technique"

- **Overview:** Brief explanation of RFS and its purpose.

- **Key Concepts:** Bullet points on feature importance, the iterative process, and model simplification.

- **Impact on Backtest Performance:** Highlight the improvement in absolute average prediction error.

- **Advantages:** Bullet points on the benefits of RFS.

- **Visual Aid:** Diagram illustrating the RFS process.

Would you like any additional details or specific formatting for this slide?

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