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Upload metrics/metrics.json with huggingface_hub

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  1. metrics/metrics.json +185 -0
metrics/metrics.json ADDED
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+ {
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+ "gold": {
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+ "type": "regression",
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+ "R2": 0.9904,
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+ "RMSE": 2.2916,
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+ "MAE": 1.2252,
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+ "unit": "$",
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+ "CV_R2": 0.9886,
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+ "best_params": {
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+ "n_estimators": 200,
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+ "min_samples_leaf": 1,
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+ "max_depth": 20
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+ }
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+ },
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+ "student": {
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+ "type": "regression",
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+ "R2": 0.8489,
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+ "RMSE": 6.0646,
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+ "MAE": 4.7104,
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+ "unit": "puan",
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+ "CV_R2": 0.8373,
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+ "best_params": {
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+ "n_estimators": 100,
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+ "min_samples_leaf": 2,
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+ "max_depth": 20
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+ }
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+ },
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+ "uber": {
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+ "type": "regression",
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+ "R2": 0.7775,
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+ "RMSE": 4.6446,
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+ "MAE": 2.2872,
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+ "unit": "$",
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+ "CV_R2": 0.7859,
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+ "best_params": {
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+ "n_estimators": 50,
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+ "min_samples_leaf": 1,
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+ "max_depth": null
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+ }
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+ },
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+ "mobile": {
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+ "type": "classification",
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+ "Accuracy": 0.8125,
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+ "Precision": 0.8184,
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+ "Recall": 0.8125,
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+ "F1": 0.8142,
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+ "CV_Accuracy": 0.8069,
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+ "best_params": {
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+ "n_estimators": 100,
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+ "min_samples_leaf": 2,
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+ "max_depth": 20
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+ }
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+ },
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+ "wine": {
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+ "type": "classification",
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+ "Accuracy": 0.675,
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+ "Precision": 0.6425,
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+ "Recall": 0.675,
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+ "F1": 0.6562,
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+ "CV_Accuracy": 0.6896,
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+ "best_params": {
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+ "n_estimators": 300,
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+ "min_samples_leaf": 1,
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+ "max_depth": 20
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+ }
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+ },
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+ "churn": {
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+ "type": "classification",
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+ "Accuracy": 0.7889,
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+ "Precision": 0.7771,
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+ "Recall": 0.7889,
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+ "F1": 0.7796,
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+ "CV_Accuracy": 0.7991,
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+ "best_params": {
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+ "n_estimators": 200,
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+ "min_samples_leaf": 1,
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+ "max_depth": 20
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+ }
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+ },
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+ "nba": {
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+ "type": "clustering",
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+ "Silhouette": 0.452,
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+ "n_clusters": 3
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+ },
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+ "creditcard": {
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+ "type": "clustering",
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+ "Silhouette": 0.5309,
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+ "n_clusters": 3
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+ },
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+ "spotify": {
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+ "type": "clustering",
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+ "Silhouette": 0.3269,
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+ "n_clusters": 4
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+ },
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+ "mask": {
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+ "type": "classification",
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+ "Accuracy": 0.825,
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+ "Precision": 0.8249,
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+ "Recall": 0.825,
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+ "F1": 0.8244,
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+ "CV_Accuracy": 0.8187
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+ },
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+ "sms": {
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+ "type": "classification",
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+ "Accuracy": 0.9803,
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+ "Precision": 0.9807,
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+ "Recall": 0.9803,
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+ "F1": 0.9796
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+ },
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+ "imdb_sentiment": {
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+ "type": "classification",
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+ "Accuracy": 0.873,
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+ "Precision": 0.8741,
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+ "Recall": 0.873,
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+ "F1": 0.8729
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+ },
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+ "fake_news": {
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+ "type": "classification",
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+ "Accuracy": 0.9756,
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+ "Precision": 0.9758,
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+ "Recall": 0.9756,
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+ "F1": 0.9756
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+ },
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+ "movie_rec": {
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+ "type": "none",
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+ "note": "İçerik tabanlı (unsupervised) öneri sistemi — accuracy/R² gibi tek bir başarı metriği yoktur."
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+ },
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+ "book_rec": {
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+ "type": "none",
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+ "note": "İçerik tabanlı (unsupervised) öneri sistemi — tek bir başarı metriği yoktur."
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+ },
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+ "song_rec": {
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+ "type": "none",
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+ "note": "İçerik tabanlı (unsupervised) öneri sistemi — tek bir başarı metriği yoktur."
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+ },
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+ "stock": {
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+ "type": "regression",
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+ "R2": 0.975,
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+ "RMSE": 4.1682,
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+ "MAE": 2.959,
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+ "unit": "$"
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+ },
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+ "weather": {
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+ "type": "regression",
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+ "R2": 0.912,
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+ "RMSE": 1.6784,
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+ "MAE": 1.2744,
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+ "unit": "°C"
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+ },
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+ "walmart": {
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+ "type": "regression",
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+ "R2": 0.7669,
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+ "RMSE": 91283.4797,
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+ "MAE": 79987.0904,
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+ "unit": "$"
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+ },
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+ "social_media_viz": {
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+ "type": "none",
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+ "note": "Bu sekmede eğitilen bir model yok, sadece görselleştirme var."
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+ },
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+ "co2_viz": {
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+ "type": "none",
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+ "note": "Bu sekmede eğitilen bir model yok, sadece görselleştirme var."
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+ },
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+ "ecommerce_viz": {
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+ "type": "none",
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+ "note": "Bu sekmede eğitilen bir model yok, sadece görselleştirme var."
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+ },
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+ "pneumonia": {
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+ "type": "deep_learning",
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+ "Accuracy": 0.9331,
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+ "Val_Accuracy": 0.925,
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+ "Loss": 0.1816
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+ },
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+ "face_emotion": {
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+ "type": "deep_learning",
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+ "Accuracy": 0.6573,
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+ "Val_Accuracy": 0.6542,
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+ "Loss": 0.7471
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+ },
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+ "text_gen": {
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+ "type": "none",
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+ "note": "Markov Zinciri üretken (generative) bir modeldir — doğruluk/hata metriğiyle değil, üretilen metnin akıcılığıyla değerlendirilir."
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+ }
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+ }