reports.json

{
  "overall": {
    "at_0.5": {
      "precision_macro": 0.8871660305343512,
      "recall_macro": 0.8966275659824048,
      "f1_macro": 0.8917858651698956,
      "precision_weighted": 0.9334314850372314,
      "recall_weighted": 0.9325153374233128,
      "f1_weighted": 0.932922632145847,
      "accuracy": 0.9325153374233128,
      "confusion_matrix": [
        [
          126,
          6
        ],
        [
          5,
          26
        ]
      ],
      "classification_report": "              precision    recall  f1-score   support\n\n no-recl (0)     0.9618    0.9545    0.9582       132\n    recl (1)     0.8125    0.8387    0.8254        31\n\n    accuracy                         0.9325       163\n   macro avg     0.8872    0.8966    0.8918       163\nweighted avg     0.9334    0.9325    0.9329       163\n",
      "threshold": 0.5
    },
    "at_best_global": {
      "precision_macro": 0.9187091503267973,
      "recall_macro": 0.875733137829912,
      "f1_macro": 0.8951363870303655,
      "precision_weighted": 0.9371867356349493,
      "recall_weighted": 0.9386503067484663,
      "f1_weighted": 0.9369926336163481,
      "accuracy": 0.9386503067484663,
      "confusion_matrix": [
        [
          129,
          3
        ],
        [
          7,
          24
        ]
      ],
      "classification_report": "              precision    recall  f1-score   support\n\n no-recl (0)     0.9485    0.9773    0.9627       132\n    recl (1)     0.8889    0.7742    0.8276        31\n\n    accuracy                         0.9387       163\n   macro avg     0.9187    0.8757    0.8951       163\nweighted avg     0.9372    0.9387    0.9370       163\n",
      "threshold": 0.55
    },
    "at_best_by_lang": {
      "precision_macro": 0.9187091503267973,
      "recall_macro": 0.875733137829912,
      "f1_macro": 0.8951363870303655,
      "precision_weighted": 0.9371867356349493,
      "recall_weighted": 0.9386503067484663,
      "f1_weighted": 0.9369926336163481,
      "accuracy": 0.9386503067484663,
      "confusion_matrix": [
        [
          129,
          3
        ],
        [
          7,
          24
        ]
      ],
      "classification_report": "              precision    recall  f1-score   support\n\n no-recl (0)     0.9485    0.9773    0.9627       132\n    recl (1)     0.8889    0.7742    0.8276        31\n\n    accuracy                         0.9387       163\n   macro avg     0.9187    0.8757    0.8951       163\nweighted avg     0.9372    0.9387    0.9370       163\n",
      "thresholds_by_lang": {
        "it": 0.55
      }
    }
  },
  "thresholds": {
    "global_best": {
      "threshold": 0.55,
      "f1_macro": 0.8951363870303655,
      "precision_macro": 0.9187091503267973,
      "recall_macro": 0.875733137829912
    },
    "by_lang_best": {
      "it": {
        "threshold": 0.55,
        "f1_macro": 0.8951363870303655,
        "precision_macro": 0.9187091503267973,
        "recall_macro": 0.875733137829912
      }
    },
    "default": 0.5
  },
  "per_lang": {
    "at_0.5": [
      {
        "lang": "it",
        "n": 163,
        "accuracy": 0.9325153374233128,
        "f1_macro": 0.8917858651698956,
        "precision_macro": 0.8871660305343512,
        "recall_macro": 0.8966275659824048,
        "f1_weighted": 0.932922632145847,
        "precision_weighted": 0.9334314850372314,
        "recall_weighted": 0.9325153374233128
      }
    ],
    "at_best_global": [
      {
        "lang": "it",
        "n": 163,
        "accuracy": 0.9386503067484663,
        "f1_macro": 0.8951363870303655,
        "precision_macro": 0.9187091503267973,
        "recall_macro": 0.875733137829912,
        "f1_weighted": 0.9369926336163481,
        "precision_weighted": 0.9371867356349493,
        "recall_weighted": 0.9386503067484663
      }
    ],
    "at_best_by_lang": [
      {
        "lang": "it",
        "n": 163,
        "accuracy": 0.9386503067484663,
        "f1_macro": 0.8951363870303655,
        "precision_macro": 0.9187091503267973,
        "recall_macro": 0.875733137829912,
        "f1_weighted": 0.9369926336163481,
        "precision_weighted": 0.9371867356349493,
        "recall_weighted": 0.9386503067484663
      }
    ]
  }
}