Logistic regression

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    "timestamp": "2026-08-01T19:14:27+00:00"
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    "timestamp": "2026-08-01T19:15:22+00:00"
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    "kind": "tabular",
    "limitations": {
     "has_real_subgroup_metadata": false,
     "n_train_available": 3769,
     "notes": null,
     "subgroup_basis": "worst diagnostic class (no demographic metadata in source)",
     "train_capped": false
    },
    "modality": "Tabular / multispectral",
    "n_classes": 6,
    "n_test": 2000,
    "runtime_s": 0.1,
    "seed": 20260727,
    "spec_version": "MedEval-1 v0.1",
    "split_origin": "official released split (Statlog sat.trn 4435 / sat.tst 2000), scored whole, with validation carved from sat.trn only (stratified 15%, seed 20260727). LEAKS, AND THE ACCURACIES HERE ARE OPTIMISTIC BECAUSE OF IT: each row is a 3x3 ground window, and 1,939 of the 2,000 test windows (97%) have an immediate neighbour in the training file sharing six of their nine ground pixels -- the two files are interleaved windows of one 82x100 scene, not separated regions. No 36-vector appears in both files, so byte-level deduplication passes while the test set overlaps training data on the ground; byte disjointness is not independence. Measured in variation/modalities/multispectral.md section 8. Raw class codes are 1,2,3,4,5,7 -- code 6 was withdrawn by the donors -- and are mapped positionally onto 0..5, not by subtracting one.",
    "task": "landsat_statlog",
    "task_name": "Landsat satellite land cover (6-class)",
    "timestamp": "2026-08-03T16:02:36+00:00"
   },
   {
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    "composite": {
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     "dimensions_scored": [
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    },
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    "dimensions": {
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      "auc": 0.980323,
      "balanced_accuracy": 0.899769,
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      "n_test": 9752,
      "score": 79.953726
     },
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     "calibration": {
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      "brier": 0.095536,
      "ece": 0.032848,
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      "overconfidence": 0.022263,
      "score": 83.575984
     },
     "corruption": {
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      "clean_reference_cc": 0.828612,
      "mean_agreement": 0.945648,
      "mean_kappa": 0.805737,
      "mean_retention": 0.884259,
      "n_cases": 1200,
      "per_perturbation": {
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         1,
         1,
         1
        ],
        "mean_kappa": 1,
        "mean_retention": 1,
        "not_applicable": "this corpus has no one-hot encoded categorical block, so there is no coding scheme to drift; the transform is a no-op here and its retention of 1.00 is an artefact, not a result",
        "retention_by_severity": [
         1,
         1,
         1
        ],
        "scored": false
       },
       "entry_error": {
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         0.9808,
         0.9608
        ],
        "mean_kappa": 0.934114,
        "mean_retention": 0.98611,
        "retention_by_severity": [
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         1,
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        ],
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       },
       "field_dropout": {
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         0.9375
        ],
        "mean_kappa": 0.892998,
        "mean_retention": 1,
        "retention_by_severity": [
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         1,
         1
        ],
        "scored": true
       },
       "unit_scale": {
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         0.9375,
         0.7783
        ],
        "mean_kappa": 0.590098,
        "mean_retention": 0.666667,
        "retention_by_severity": [
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         1,
         0
        ],
        "scored": true
       }
      },
      "score": 88.425888,
      "worst_retention": 0
     },
     "cost": null,
     "limited_data": {
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      "mean_retention": 0.968404,
      "per_budget": {
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        "n_labels": 200,
        "retention": 1
       },
       "n20": {
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        "n_labels": 40,
        "retention": 1
       },
       "n5": {
        "chance_corrected": 0.723751,
        "n_labels": 10,
        "retention": 0.905212
       }
      },
      "score": 96.840405
     },
     "spec_sensitivity": null,
     "subgroup": {
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      "basis": "worst diagnostic class (no demographic metadata in source)",
      "has_real_metadata": false,
      "score": 74.914592,
      "worst_class_recall": 0.874573
     },
     "uncertainty": {
      "full_accuracy": 0.914377,
      "risk_coverage_auc": 0.992062,
      "score": 98.412349,
      "selective_acc_at_50": 0.99959,
      "selective_acc_at_80": 0.995642
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    },
    "domain": "energy",
    "kind": "tabular",
    "limitations": {
     "has_real_subgroup_metadata": false,
     "n_train_available": 6922,
     "notes": null,
     "subgroup_basis": "worst diagnostic class (no demographic metadata in source)",
     "train_capped": false
    },
    "modality": "Tabular / environmental sensors",
    "n_classes": 2,
    "n_test": 9752,
    "runtime_s": 0.1,
    "seed": 20260727,
    "spec_version": "MedEval-1 v0.1",
    "split_origin": "official released split: the donor released one training period (datatraining.txt, 8143 rows) and two separate later test periods (datatest.txt 2665 rows, datatest2.txt 9752 rows); THIS task scores test period 2 (datatest2.txt) whole and never fits on it. Validation is the last 15% of the training period in time order, not a random carve -- consecutive rows are one minute apart, so a random validation set would be scored on rows whose own neighbours are in train.",
    "task": "occupancy_detection_2",
    "task_name": "Room occupancy from sensors (period 2)",
    "timestamp": "2026-08-01T19:15:19+00:00"
   },
   {
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    "composite": {
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     "dimensions_scored": [
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     "index": 92.79503
    },
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    "dimensions": {
     "accuracy": {
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      "auc": 0.992121,
      "balanced_accuracy": 0.977365,
      "ci95": [
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      "n_test": 2665,
      "score": 95.472947
     },
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     "calibration": {
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      "brier": 0.041825,
      "ece": 0.020148,
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      "overconfidence": -0.014478,
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      "mean_agreement": 0.951574,
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      "mean_retention": 0.88191,
      "n_cases": 1200,
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         1,
         1
        ],
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        "mean_retention": 1,
        "not_applicable": "this corpus has no one-hot encoded categorical block, so there is no coding scheme to drift; the transform is a no-op here and its retention of 1.00 is an artefact, not a result",
        "retention_by_severity": [
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         1,
         1
        ],
        "scored": false
       },
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         0.9675
        ],
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        "mean_retention": 0.978229,
        "retention_by_severity": [
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         0.9436
        ],
        "scored": true
       },
       "field_dropout": {
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         0.9967
        ],
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        "mean_retention": 1,
        "retention_by_severity": [
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         1,
         1
        ],
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       "unit_scale": {
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         0.6308
        ],
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        "retention_by_severity": [
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         1,
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        ],
        "scored": true
       }
      },
      "score": 88.191024,
      "worst_retention": 0.002504
     },
     "cost": null,
     "limited_data": {
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      "mean_retention": 0.929659,
      "per_budget": {
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        "n_labels": 200,
        "retention": 1
       },
       "n20": {
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        "n_labels": 40,
        "retention": 0.938071
       },
       "n5": {
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        "n_labels": 10,
        "retention": 0.850906
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      },
      "score": 92.965901
     },
     "spec_sensitivity": null,
     "subgroup": {
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      "basis": "worst diagnostic class (no demographic metadata in source)",
      "has_real_metadata": false,
      "score": 93.620791,
      "worst_class_recall": 0.968104
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     "uncertainty": {
      "full_accuracy": 0.974859,
      "risk_coverage_auc": 0.99719,
      "score": 99.438032,
      "selective_acc_at_50": 1,
      "selective_acc_at_80": 0.99531
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    "kind": "tabular",
    "limitations": {
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     "n_train_available": 6922,
     "notes": null,
     "subgroup_basis": "worst diagnostic class (no demographic metadata in source)",
     "train_capped": false
    },
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    "n_classes": 2,
    "n_test": 2665,
    "runtime_s": 0.1,
    "seed": 20260727,
    "spec_version": "MedEval-1 v0.1",
    "split_origin": "official released split: the donor released one training period (datatraining.txt, 8143 rows) and two separate later test periods (datatest.txt 2665 rows, datatest2.txt 9752 rows); THIS task scores test period 1 (datatest.txt) whole and never fits on it. Validation is the last 15% of the training period in time order, not a random carve -- consecutive rows are one minute apart, so a random validation set would be scored on rows whose own neighbours are in train.",
    "task": "occupancy_detection",
    "task_name": "Room occupancy from sensors (period 1)",
    "timestamp": "2026-08-01T19:15:16+00:00"
   },
   {
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    "composite": {
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     "dimensions_scored": [
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    "dimensions": {
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      "auc": 0.608696,
      "balanced_accuracy": 0.493207,
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      "score": 0
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      "brier": 0.149519,
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      "clean_reference_cc": 0,
      "mean_agreement": null,
      "mean_kappa": null,
      "mean_retention": null,
      "n_cases": 392,
      "not_scored_reason": "the clean model scores at or below chance on this corpus (chance-corrected balanced accuracy 0.0000), so there is no retained discrimination to measure a ratio against; retention is reported as measured but not scored, because a 0.00 here would be the metric's arithmetic rather than the model's robustness",
      "per_perturbation": {
       "coding_drift": {
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         1,
         1
        ],
        "mean_kappa": 1,
        "mean_retention": 0,
        "not_applicable": "this corpus has no one-hot encoded categorical block, so there is no coding scheme to drift; the transform is a no-op here and its retention of 1.00 is an artefact, not a result",
        "retention_by_severity": [
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         0,
         0
        ],
        "scored": false
       },
       "entry_error": {
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         0.9388
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        "mean_retention": 0,
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        "retention_by_severity": [
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         0,
         0
        ],
        "scored": false
       },
       "field_dropout": {
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         0.9949,
         0.9643
        ],
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        "mean_retention": 0,
        "not_applicable": "the clean model scores at or below chance on this corpus (chance-corrected balanced accuracy 0.0000), so there is no retained discrimination to measure a ratio against; retention is reported as measured but not scored, because a 0.00 here would be the metric's arithmetic rather than the model's robustness",
        "retention_by_severity": [
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         0,
         0
        ],
        "scored": false
       },
       "unit_scale": {
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        "mean_retention": 0,
        "not_applicable": "the clean model scores at or below chance on this corpus (chance-corrected balanced accuracy 0.0000), so there is no retained discrimination to measure a ratio against; retention is reported as measured but not scored, because a 0.00 here would be the metric's arithmetic rather than the model's robustness",
        "retention_by_severity": [
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         0,
         0
        ],
        "scored": false
       }
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      "score": null,
      "worst_retention": null
     },
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     "limited_data": {
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      "per_budget": {
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        "retention": 0
       },
       "n20": {
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        "n_labels": 40,
        "retention": 0
       },
       "n5": {
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        "n_labels": 10,
        "retention": 0
       }
      },
      "score": 0
     },
     "spec_sensitivity": null,
     "subgroup": {
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      "has_real_metadata": false,
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     },
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      "selective_acc_at_50": 0.943878,
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    },
    "domain": "industrial",
    "kind": "tabular",
    "limitations": {
     "has_real_subgroup_metadata": false,
     "n_train_available": 940,
     "notes": null,
     "subgroup_basis": "worst diagnostic class (no demographic metadata in source)",
     "train_capped": false
    },
    "modality": "Tabular / process sensors",
    "n_classes": 2,
    "n_test": 392,
    "runtime_s": 0.1,
    "seed": 20260727,
    "spec_version": "MedEval-1 v0.1",
    "split_origin": "chronological split minted by MedEval-1 on the production timestamp shipped in secom_labels.data: earliest 60% train, next 15% validation, latest 25% test. A random split is not defensible on a process-monitoring corpus -- tools drift and are recalibrated, so shuffling lets the model interpolate a tool's state from wafers measured minutes on either side, while production always extrapolates forward. Missing values imputed with the TRAIN median only; channels all-missing or constant on the training period dropped (468 of 590 channels kept).",
    "task": "secom",
    "task_name": "Semiconductor fabrication pass/fail",
    "timestamp": "2026-08-01T19:13:53+00:00"
   },
   {
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     "dimensions_scored": [
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    "dimensions": {
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      "n_test": 486,
      "score": 70.434925
     },
     "acquisition": null,
     "calibration": {
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      "brier": 0.405677,
      "ece": 0.067992,
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      "score": 66.004218
     },
     "corruption": {
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      "clean_reference_cc": 0.704349,
      "mean_agreement": 0.758116,
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      "mean_retention": 0.661451,
      "n_cases": 486,
      "per_perturbation": {
       "coding_drift": {
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         1,
         1,
         1
        ],
        "mean_kappa": 1,
        "mean_retention": 1,
        "not_applicable": "this corpus has no one-hot encoded categorical block, so there is no coding scheme to drift; the transform is a no-op here and its retention of 1.00 is an artefact, not a result",
        "retention_by_severity": [
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         1,
         1
        ],
        "scored": false
       },
       "entry_error": {
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         0.7551
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        "retention_by_severity": [
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        "scored": true
       },
       "field_dropout": {
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        "mean_kappa": 0.514781,
        "mean_retention": 0.486918,
        "retention_by_severity": [
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         0.241
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        "scored": true
       },
       "unit_scale": {
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         0.5432
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        "mean_kappa": 0.654336,
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         0.2235
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        "scored": true
       }
      },
      "score": 66.145126,
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     },
     "cost": null,
     "limited_data": {
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      "mean_retention": 0.925794,
      "per_budget": {
       "n100": {
        "chance_corrected": 0.723243,
        "n_labels": 571,
        "retention": 1
       },
       "n20": {
        "chance_corrected": 0.635584,
        "n_labels": 140,
        "retention": 0.902371
       },
       "n5": {
        "chance_corrected": 0.616314,
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        "retention": 0.875012
       }
      },
      "score": 92.579436
     },
     "spec_sensitivity": null,
     "subgroup": {
      "attributes": {
       "steel_type": {
        "gap": 0.182203,
        "per_group": {
         "A300": 0.435794,
         "A400": 0.617997
        },
        "worst": 0.435794
       }
      },
      "basis": "worst demographic subgroup (real metadata)",
      "has_real_metadata": true,
      "score": 34.175968,
      "worst_class_recall": 0.564356
     },
     "uncertainty": {
      "full_accuracy": 0.713992,
      "risk_coverage_auc": 0.849393,
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      "selective_acc_at_50": 0.82716,
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    },
    "domain": "industrial",
    "kind": "tabular",
    "limitations": {
     "has_real_subgroup_metadata": true,
     "n_train_available": 1164,
     "notes": null,
     "subgroup_basis": "worst demographic subgroup (real metadata)",
     "train_capped": false
    },
    "modality": "Tabular / imaging-derived",
    "n_classes": 7,
    "n_test": 486,
    "runtime_s": 0.1,
    "seed": 20260727,
    "spec_version": "MedEval-1 v0.1",
    "split_origin": "minted by MedEval-1 (stratified 60/15/25, seed 20260727); no official split is published for this corpus. The label is the argmax of the seven trailing one-hot fault columns, which are dropped from the features.",
    "task": "steel_plate_faults",
    "task_name": "Steel plate surface faults (7-class)",
    "timestamp": "2026-08-01T19:13:43+00:00"
   },
   {
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     "dimensions_scored": [
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     },
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     "calibration": {
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      "score": 69.318061
     },
     "corruption": {
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      "clean_reference_cc": 0.503869,
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      "per_perturbation": {
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         1
        ],
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