Model Quality Metrics

Model quality monitoring jobs compute different metrics depending on the ML problem type. The following sections list the metrics analyzed for each ML problem type.

Note

Standard deviation for metrics are provided only when at least 200 samples are available. Model Monitor computes standard deviation by randomly sampling 80% of the data 5 times, computing the metric, and taking the standard deviation for those results.

Regression Metrics

The following shows an example of the metrics that model quality monitor computes for a regression problem.

"regression_metrics" : {
    "mae" : {
      "value" : 0.3711832061068702,
      "standard_deviation" : 0.0037566388129940394
    },
    "mse" : {
      "value" : 0.3711832061068702,
      "standard_deviation" : 0.0037566388129940524
    },
    "rmse" : {
      "value" : 0.609248066149471,
      "standard_deviation" : 0.003079253267651125
    },
    "r2" : {
      "value" : -1.3766111872212665,
      "standard_deviation" : 0.022653980022771227
    }
  }

Binary Classification Metrics

The following shows an example of the metrics that model quality monitor computes for a binary classification problem.

"binary_classification_metrics" : {
    "confusion_matrix" : {
      "0" : {
        "0" : 1,
        "1" : 2
      },
      "1" : {
        "0" : 0,
        "1" : 1
      }
    },
    "recall" : {
      "value" : 1.0,
      "standard_deviation" : "NaN"
    },
    "precision" : {
      "value" : 0.3333333333333333,
      "standard_deviation" : "NaN"
    },
    "accuracy" : {
      "value" : 0.5,
      "standard_deviation" : "NaN"
    },
    "recall_best_constant_classifier" : {
      "value" : 1.0,
      "standard_deviation" : "NaN"
    },
    "precision_best_constant_classifier" : {
      "value" : 0.25,
      "standard_deviation" : "NaN"
    },
    "accuracy_best_constant_classifier" : {
      "value" : 0.25,
      "standard_deviation" : "NaN"
    },
    "true_positive_rate" : {
      "value" : 1.0,
      "standard_deviation" : "NaN"
    },
    "true_negative_rate" : {
      "value" : 0.33333333333333337,
      "standard_deviation" : "NaN"
    },
    "false_positive_rate" : {
      "value" : 0.6666666666666666,
      "standard_deviation" : "NaN"
    },
    "false_negative_rate" : {
      "value" : 0.0,
      "standard_deviation" : "NaN"
    },
    "receiver_operating_characteristic_curve" : {
      "false_positive_rates" : [ 0.0, 0.0, 0.0, 0.0, 0.0, 1.0 ],
      "true_positive_rates" : [ 0.0, 0.25, 0.5, 0.75, 1.0, 1.0 ]
    },
    "precision_recall_curve" : {
      "precisions" : [ 1.0, 1.0, 1.0, 1.0, 1.0 ],
      "recalls" : [ 0.0, 0.25, 0.5, 0.75, 1.0 ]
    },
    "auc" : {
      "value" : 1.0,
      "standard_deviation" : "NaN"
    },
    "f0_5" : {
      "value" : 0.3846153846153846,
      "standard_deviation" : "NaN"
    },
    "f1" : {
      "value" : 0.5,
      "standard_deviation" : "NaN"
    },
    "f2" : {
      "value" : 0.7142857142857143,
      "standard_deviation" : "NaN"
    },
    "f0_5_best_constant_classifier" : {
      "value" : 0.29411764705882354,
      "standard_deviation" : "NaN"
    },
    "f1_best_constant_classifier" : {
      "value" : 0.4,
      "standard_deviation" : "NaN"
    },
    "f2_best_constant_classifier" : {
      "value" : 0.625,
      "standard_deviation" : "NaN"
    }
  }

Multiclass Metrics

The following shows an example of the metrics that model quality monitor computes for a multiclass classification problem.

"multiclass_classification_metrics" : {
    "confusion_matrix" : {
      "0" : {
        "0" : 1180,
        "1" : 510
      },
      "1" : {
        "0" : 268,
        "1" : 138
      }
    },
    "accuracy" : {
      "value" : 0.6288167938931297,
      "standard_deviation" : 0.00375663881299405
    },
    "weighted_recall" : {
      "value" : 0.6288167938931297,
      "standard_deviation" : 0.003756638812994008
    },
    "weighted_precision" : {
      "value" : 0.6983172269629505,
      "standard_deviation" : 0.006195912915307507
    },
    "weighted_f0_5" : {
      "value" : 0.6803947317178771,
      "standard_deviation" : 0.005328406973561699
    },
    "weighted_f1" : {
      "value" : 0.6571162346664904,
      "standard_deviation" : 0.004385008075019733
    },
    "weighted_f2" : {
      "value" : 0.6384024354394601,
      "standard_deviation" : 0.003867109755267757
    },
    "accuracy_best_constant_classifier" : {
      "value" : 0.19370229007633588,
      "standard_deviation" : 0.0032049848450732355
    },
    "weighted_recall_best_constant_classifier" : {
      "value" : 0.19370229007633588,
      "standard_deviation" : 0.0032049848450732355
    },
    "weighted_precision_best_constant_classifier" : {
      "value" : 0.03752057718081697,
      "standard_deviation" : 0.001241536088657851
    },
    "weighted_f0_5_best_constant_classifier" : {
      "value" : 0.04473443104152011,
      "standard_deviation" : 0.0014460485504284792
    },
    "weighted_f1_best_constant_classifier" : {
      "value" : 0.06286421244683643,
      "standard_deviation" : 0.0019113576884608862
    },
    "weighted_f2_best_constant_classifier" : {
      "value" : 0.10570313141262414,
      "standard_deviation" : 0.002734216826748117
    }
  }