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У вас уже есть ответвление SniperGold_ML
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ответвлён от chiki2bum2/SniperGold_ML
SniperGold_ML/ml/p3/baseline/output/p3_s18_baseline_results.json

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{
"generated_utc": "2026-08-24T00:00:11.561916+00:00",
"models": [
"logistic",
"tree_depth3",
"boost_small",
"mlp16"
],
"metric_sets": {
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"balanced_acc": 0.7055393586005831,
"win_precision": 1.0,
"win_recall": 0.019417475728155338,
"n": 343,
"n_win": 103,
"n_loss": 240
},
"val": {
"roc_auc": 0.5991967871485944,
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"log_loss": 0.5757370722839635,
"brier": 0.19327589499702091,
"balanced_acc": 0.7345132743362832,
"win_precision": 0.0,
"win_recall": 0.0,
"n": 113,
"n_win": 30,
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},
"test": {
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"log_loss": 0.59967333716527,
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"balanced_acc": 0.6695652173913044,
"win_precision": 0.3,
"win_recall": 0.08823529411764706,
"n": 115,
"n_win": 34,
"n_loss": 81
}
},
"tree_depth3": {
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"pr_auc": 0.3994440749167371,
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},
"val": {
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"log_loss": 1.2236177374353694,
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"balanced_acc": 0.6460176991150443,
"win_precision": 0.1875,
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"n": 113,
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"n_loss": 83
},
"test": {
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"win_precision": 0.25,
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"n": 115,
"n_win": 34,
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}
},
"boost_small": {
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"log_loss": 0.5771092623317469,
"brier": 0.1958119433556162,
"balanced_acc": 0.7026239067055393,
"win_precision": 1.0,
"win_recall": 0.009708737864077669,
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"n_loss": 240
},
"val": {
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"n": 113,
"n_win": 30,
"n_loss": 83
},
"test": {
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"pr_auc": 0.37519583495879855,
"log_loss": 0.6162951146263919,
"brier": 0.21323473763896028,
"balanced_acc": 0.6782608695652174,
"win_precision": 0.3333333333333333,
"win_recall": 0.08823529411764706,
"n": 115,
"n_win": 34,
"n_loss": 81
}
},
"mlp16": {
"train": {
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"pr_auc": 0.6796298365421298,
"log_loss": 0.4588743953108605,
"brier": 0.15056723883592377,
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"win_recall": 0.39805825242718446,
"n": 343,
"n_win": 103,
"n_loss": 240
},
"val": {
"roc_auc": 0.3887550200803213,
"pr_auc": 0.22147127665899768,
"log_loss": 0.7873656060915675,
"brier": 0.2632889575142873,
"balanced_acc": 0.6106194690265486,
"win_precision": 0.15,
"win_recall": 0.1,
"n": 113,
"n_win": 30,
"n_loss": 83
},
"test": {
"roc_auc": 0.5954974582425562,
"pr_auc": 0.3393685314234896,
"log_loss": 0.8985939428976393,
"brier": 0.3073426618221339,
"balanced_acc": 0.5826086956521739,
"win_precision": 0.390625,
"win_recall": 0.7352941176470589,
"n": 115,
"n_win": 34,
"n_loss": 81
}
}
},
"test_confusion": [
{
"model": "logistic",
"test_WIN_predWIN": 3,
"test_WIN_predLOSS": 31,
"test_LOSS_predWIN": 7,
"test_LOSS_predLOSS": 74
},
{
"model": "tree_depth3",
"test_WIN_predWIN": 3,
"test_WIN_predLOSS": 31,
"test_LOSS_predWIN": 9,
"test_LOSS_predLOSS": 72
},
{
"model": "boost_small",
"test_WIN_predWIN": 3,
"test_WIN_predLOSS": 31,
"test_LOSS_predWIN": 6,
"test_LOSS_predLOSS": 75
},
{
"model": "mlp16",
"test_WIN_predWIN": 25,
"test_WIN_predLOSS": 9,
"test_LOSS_predWIN": 39,
"test_LOSS_predLOSS": 42
}
],
"unresolved_ambiguous_counts": {
"all_unresolved": 22,
"all_ambiguous": 5,
"lead_unresolved": 18,
"lead_ambiguous": 5,
"note": "UNRESOLVED/AMBIGUOUS retained in the dataset, excluded from the binary fit, reported here (never destroyed)."
},
"feature_sha16": "0414e401522ea4e2"
}