Here’s the variable importance ranking for your dataset and task:

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## 🔑 Variable Importance Ranking

**Task: minimise `abs_lam_diff` (absolute λ-difference, i.e. electronic transition / optical absorption property difference).**

1. **r5\_label**

   * **Reasoning:**

     * Largest chemical diversity (11 labels → widest substituent variation).
     * Substituents at position 5 likely conjugated into the π-system, strongly altering electron density and absorption properties.
     * High probability of dominating shifts in excitation energies → biggest driver of `abs_lam_diff`.

2. **r3\_label**

   * **Reasoning:**

     * Eight substituents with likely resonance/electronic donation/withdrawal effects.
     * Position 3 often close to conjugation pathway in redox-active cores → modifies redox potential and optical transitions.
     * More chemical variability than r1, so higher effect.

3. **r4\_label**

   * **Reasoning:**

     * Also eight substituents, but position 4 typically more steric (para/meta vs. conjugated position depending on scaffold).
     * Moderate but secondary impact compared to r3, since electronic coupling is weaker.

4. **r1\_label**

   * **Reasoning:**

     * Only two options (lowest diversity).
     * At position 1, substituents may be less conjugated or more sterically remote.
     * Contributes less to absorption wavelength tuning compared to positions 3–5.

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## 📊 Final Order

**r5\_label > r3\_label > r4\_label > r1\_label**

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Would you like me to also suggest a **statistical test/ML approach** (e.g. ANOVA, mutual information, SHAP on a baseline model) to confirm this ranking quantitatively on your dataset?
