Here’s a principled variable ranking for maximizing **`fluo_rate`**:

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## 🔑 Variable Importance Ranking

1. **`frag_c` (central core)**

   * **Domain rationale**: In π-conjugated organic dyes, the central core dictates conjugation length, rigidity, and electronic delocalization. These strongly affect oscillator strength and radiative decay rates → direct control of **fluorescence rate**.
   * **Statistical expectation**: Largest categorical space (19 variants), so higher potential variance in `fluo_rate`.
   * **Business/optimization objective**: Swapping the core is the most effective knob for tuning fundamental photophysics.

2. **`frag_b` (π-bridge unit)**

   * **Domain rationale**: Bridges control planarity, torsional barriers, and π-conjugation extension, which modulate both spectral overlap and radiative lifetimes.
   * **Statistical expectation**: Intermediate categorical size (13 variants), likely to interact strongly with `frag_c`.
   * **Contribution**: Secondary but still substantial; modulates how the central core couples to donors/acceptors.

3. **`frag_a` (terminal donor/acceptor unit)**

   * **Domain rationale**: End groups tune charge-transfer character and absorption/emission energies. These affect *where* emission happens, but less directly the *rate* of fluorescence (since radiative rate depends more on core oscillator strength than end-group polarity).
   * **Statistical expectation**: Smallest categorical space (14 variants), and effects are often subtler or context-dependent (strong interaction with solvent/environment, less predictable in vacuum screening).
   * **Business/optimization objective**: Useful for fine-tuning, but not the primary driver of `fluo_rate`.

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## 📊 Final Ranking (most → least important)

**`frag_c` > `frag_b` > `frag_a`**

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Would you like me to also sketch out a **quantitative plan** (e.g., mutual information / ANOVA F-tests / SHAP importance after a quick model) to empirically validate this ranking on your dataset?
