evaluation_results.json

{
  "overall_metrics": {
    "accuracy": 0.85,
    "f1_macro": 0.8786689915722175,
    "f1_weighted": 0.8497929380993898,
    "f1_micro": 0.85,
    "precision_macro": 0.8793354707082482,
    "recall_macro": 0.8781642512077296
  },
  "per_class_metrics": {
    "negative": {
      "precision": 0.8297872340425532,
      "recall": 0.8478260869565217,
      "f1-score": 0.8387096774193549,
      "support": 92.0
    },
    "neutral": {
      "precision": 1.0,
      "recall": 1.0,
      "f1-score": 1.0,
      "support": 33.0
    },
    "positive": {
      "precision": 0.8082191780821918,
      "recall": 0.7866666666666666,
      "f1-score": 0.7972972972972973,
      "support": 75.0
    },
    "accuracy": 0.85,
    "macro avg": {
      "precision": 0.8793354707082482,
      "recall": 0.8781642512077296,
      "f1-score": 0.8786689915722175,
      "support": 200.0
    },
    "weighted avg": {
      "precision": 0.8497843194403965,
      "recall": 0.85,
      "f1-score": 0.8497929380993898,
      "support": 200.0
    }
  },
  "confusion_matrix": [
    [
      78,
      0,
      14
    ],
    [
      0,
      33,
      0
    ],
    [
      16,
      0,
      59
    ]
  ],
  "class_names": [
    "negative",
    "neutral",
    "positive"
  ],
  "roc_auc_scores": {}
}