metrics/metrics.json

{
  "gold": {
    "type": "regression",
    "R2": 0.9904,
    "RMSE": 2.2916,
    "MAE": 1.2252,
    "unit": "$",
    "CV_R2": 0.9886,
    "best_params": {
      "n_estimators": 200,
      "min_samples_leaf": 1,
      "max_depth": 20
    }
  },
  "student": {
    "type": "regression",
    "R2": 0.8489,
    "RMSE": 6.0646,
    "MAE": 4.7104,
    "unit": "puan",
    "CV_R2": 0.8373,
    "best_params": {
      "n_estimators": 100,
      "min_samples_leaf": 2,
      "max_depth": 20
    }
  },
  "uber": {
    "type": "regression",
    "R2": 0.7775,
    "RMSE": 4.6446,
    "MAE": 2.2872,
    "unit": "$",
    "CV_R2": 0.7859,
    "best_params": {
      "n_estimators": 50,
      "min_samples_leaf": 1,
      "max_depth": null
    }
  },
  "mobile": {
    "type": "classification",
    "Accuracy": 0.8125,
    "Precision": 0.8184,
    "Recall": 0.8125,
    "F1": 0.8142,
    "CV_Accuracy": 0.8069,
    "best_params": {
      "n_estimators": 100,
      "min_samples_leaf": 2,
      "max_depth": 20
    }
  },
  "wine": {
    "type": "classification",
    "Accuracy": 0.675,
    "Precision": 0.6425,
    "Recall": 0.675,
    "F1": 0.6562,
    "CV_Accuracy": 0.6896,
    "best_params": {
      "n_estimators": 300,
      "min_samples_leaf": 1,
      "max_depth": 20
    }
  },
  "churn": {
    "type": "classification",
    "Accuracy": 0.7889,
    "Precision": 0.7771,
    "Recall": 0.7889,
    "F1": 0.7796,
    "CV_Accuracy": 0.7991,
    "best_params": {
      "n_estimators": 200,
      "min_samples_leaf": 1,
      "max_depth": 20
    }
  },
  "nba": {
    "type": "clustering",
    "Silhouette": 0.452,
    "n_clusters": 3
  },
  "creditcard": {
    "type": "clustering",
    "Silhouette": 0.5309,
    "n_clusters": 3
  },
  "spotify": {
    "type": "clustering",
    "Silhouette": 0.3269,
    "n_clusters": 4
  },
  "mask": {
    "type": "classification",
    "Accuracy": 0.825,
    "Precision": 0.8249,
    "Recall": 0.825,
    "F1": 0.8244,
    "CV_Accuracy": 0.8187
  },
  "sms": {
    "type": "classification",
    "Accuracy": 0.9803,
    "Precision": 0.9807,
    "Recall": 0.9803,
    "F1": 0.9796
  },
  "imdb_sentiment": {
    "type": "classification",
    "Accuracy": 0.873,
    "Precision": 0.8741,
    "Recall": 0.873,
    "F1": 0.8729
  },
  "fake_news": {
    "type": "classification",
    "Accuracy": 0.9756,
    "Precision": 0.9758,
    "Recall": 0.9756,
    "F1": 0.9756
  },
  "movie_rec": {
    "type": "none",
    "note": "İçerik tabanlı (unsupervised) öneri sistemi — accuracy/R² gibi tek bir başarı metriği yoktur."
  },
  "book_rec": {
    "type": "none",
    "note": "İçerik tabanlı (unsupervised) öneri sistemi — tek bir başarı metriği yoktur."
  },
  "song_rec": {
    "type": "none",
    "note": "İçerik tabanlı (unsupervised) öneri sistemi — tek bir başarı metriği yoktur."
  },
  "stock": {
    "type": "regression",
    "R2": 0.975,
    "RMSE": 4.1682,
    "MAE": 2.959,
    "unit": "$"
  },
  "weather": {
    "type": "regression",
    "R2": 0.912,
    "RMSE": 1.6784,
    "MAE": 1.2744,
    "unit": "°C"
  },
  "walmart": {
    "type": "regression",
    "R2": 0.7669,
    "RMSE": 91283.4797,
    "MAE": 79987.0904,
    "unit": "$"
  },
  "social_media_viz": {
    "type": "none",
    "note": "Bu sekmede eğitilen bir model yok, sadece görselleştirme var."
  },
  "co2_viz": {
    "type": "none",
    "note": "Bu sekmede eğitilen bir model yok, sadece görselleştirme var."
  },
  "ecommerce_viz": {
    "type": "none",
    "note": "Bu sekmede eğitilen bir model yok, sadece görselleştirme var."
  },
  "pneumonia": {
    "type": "deep_learning",
    "Accuracy": 0.9331,
    "Val_Accuracy": 0.925,
    "Loss": 0.1816
  },
  "face_emotion": {
    "type": "deep_learning",
    "Accuracy": 0.6573,
    "Val_Accuracy": 0.6542,
    "Loss": 0.7471
  },
  "text_gen": {
    "type": "none",
    "note": "Markov Zinciri üretken (generative) bir modeldir — doğruluk/hata metriğiyle değil, üretilen metnin akıcılığıyla değerlendirilir."
  }
}