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README.md
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---
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**Evaluation Results**
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*Classification Report*
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precision recall f1-score support
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High 0.31 0.43 0.36 1609
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Low 0.40 0.32 0.36 2599
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Moderate 0.20 0.16 0.17 1772
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Non-burnable 0.82 0.89 0.85 5091
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Very_High 0.36 0.54 0.43 1438
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Very_Low 0.80 0.69 0.74 8448
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Water 0.67 0.93 0.78 584
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accuracy 0.63 21541
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macro avg 0.51 0.57 0.53 21541
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weighted avg 0.64 0.63 0.63 21541
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*Confusion Matrix*
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[[ 686 74 221 54 451 107 16]
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[ 317 842 302 76 296 733 33]
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[ 578 202 276 58 402 237 19]
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[ 20 75 50 4536 37 309 64]
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[ 358 45 213 5 783 30 4]
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[ 282 877 329 786 213 5829 132]
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[ 7 7 1 8 2 15 544]]
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**Notes**
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-Overall accuracy: 0.63 or 63%
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**Best performing classes:**
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-Non-burnable (F1: 0.85)
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-Very_Low (F1: 0.74)
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-Water (F1: 0.78)
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**Weaker performance on:**
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-Moderate
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-High
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-Low
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**DISCLOSURES**
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-Model was developed with assistance from Github Copilot Only for the augmentation techniques. (MIXUP, CUTMIX)
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