metrics.json

{
  "train_accuracy": 0.92105,
  "test_accuracy": 0.9,
  "train_test_gap": 0.021050000000000013,
  "accuracy": 0.9,
  "precision": 0.9266284002994759,
  "recall": 0.9474355702985455,
  "f1": 0.936916477416099,
  "cv_f1_mean": 0.9401495092909122,
  "cv_f1_std": 0.0012473954226150477,
  "train_time_s": 0.3797340393066406,
  "features": [
    "num_drivers",
    "avg_effort",
    "std_dev",
    "max_gap",
    "gini_index",
    "min_effort",
    "max_effort",
    "outlier_count",
    "pct_above_avg",
    "effort_cv",
    "skewness",
    "kurtosis"
  ],
  "feature_importances": {
    "num_drivers": 0.02546272985637188,
    "avg_effort": 0.01513003557920456,
    "std_dev": 0.17059257626533508,
    "max_gap": 0.5543361902236938,
    "gini_index": 0.058541759848594666,
    "min_effort": 0.015202270820736885,
    "max_effort": 0.020937765017151833,
    "outlier_count": 0.060532066971063614,
    "pct_above_avg": 0.013787146657705307,
    "effort_cv": 0.033429812639951706,
    "skewness": 0.014470207504928112,
    "kurtosis": 0.017577486112713814
  },
  "class_labels": [
    "ACCEPT",
    "REOPTIMIZE"
  ],
  "confusion_matrix": [
    [
      787,
      294
    ],
    [
      206,
      3713
    ]
  ],
  "train_samples": 20000,
  "test_samples": 5000,
  "version": "v2",
  "changes": "Realistic data with boundary ambiguity, label noise, diverse effort distributions, measurement error, extra features (skewness, kurtosis)"
}