docstr: Mean Anomaly Score. For Isolation Forest represents the average of all tree-path lengths.
  
  :examples:
  
  >>> from h2o.estimators.isolation_forest import H2OIsolationForestEstimator
  >>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv")
  >>> test = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_test.csv")
  >>> isofor_model = H2OIsolationForestEstimator(sample_size=5, ntrees=7)
  >>> isofor_model.train(training_frame = train)
  >>> perf = isofor_model.model_performance()
  >>> perf.mean_score()
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code: def mean_score(self):
      """
      Mean Anomaly Score. For Isolation Forest represents the average of all tree-path lengths.
  
      :examples:
  
      >>> from h2o.estimators.isolation_forest import H2OIsolationForestEstimator
      >>> train = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_train.csv")
      >>> test = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/anomaly/ecg_discord_test.csv")
      >>> isofor_model = H2OIsolationForestEstimator(sample_size=5, ntrees=7)
      >>> isofor_model.train(training_frame = train)
      >>> perf = isofor_model.model_performance()
      >>> perf.mean_score()
      """
      if MetricsBase._has(self._metric_json, "mean_score"):
          return self._metric_json["mean_score"]
      return None
  
imports: import h2o
  from h2o.model import MetricsBase
label_desc: Use image node_id 2 for calling the H2OAnomalyDetectionModelMetrics obj's underlying member method code with example usage: obj.mean_score() and returns: None, self