Performance and feature importance list of random forest model
| Metric | Score |
|---|---|
| Model performance | |
| Mean squared error (MSE) | 0.0053 |
| R² score | 0.7272 |
| Top 10 most important features | |
| Feature | Importance |
| Upper quartile – average total remuneration ($) | 0.267394 |
| Lower quartile % women | 0.233624 |
| Upper quartile % women | 0.229031 |
| Lower quartile – average total remuneration ($) | 0.056409 |
| Lower–middle quartile % women | 0.038275 |
| Lower–middle quartile – average total remuneration ($) | 0.037101 |
| Total workforce % women | 0.018024 |
| Upper–middle quartile % women | 0.017547 |
| Upper–middle quartile – average total remuneration ($) | 0.016324 |
| Employer name_encoded | 0.013145 |
| Metric | Score |
|---|---|
| Mean squared error ( | 0.0053 |
| 0.7272 | |
| Feature | Importance |
| Upper quartile – average total remuneration ($) | 0.267394 |
| Lower quartile % women | 0.233624 |
| Upper quartile % women | 0.229031 |
| Lower quartile – average total remuneration ($) | 0.056409 |
| Lower–middle quartile % women | 0.038275 |
| Lower–middle quartile – average total remuneration ($) | 0.037101 |
| Total workforce % women | 0.018024 |
| Upper–middle quartile % women | 0.017547 |
| Upper–middle quartile – average total remuneration ($) | 0.016324 |
| Employer name_encoded | 0.013145 |
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