hoo@stat1007:~$ cat draftquality/model_info/enwiki.draft_quality.md 
Model Information:
         - type: GradientBoosting
         - version: 0.2.0
         - params: {'max_depth': 5, 'min_impurity_decrease': 0.0, 'multilabel': False, 'min_weight_fraction_leaf': 0.0, 'scale': False, 'label_weights': None, 'random_state': None, 'subsample': 1.0, 'min_impurity_split': None, 'max_features': 'log2', 'n_estimators': 300, 'labels': ['OK', 'spam', 'vandalism', 'attack'], 'presort': 'auto', 'max_leaf_nodes': None, 'learning_rate': 0.1, 'center': False, 'verbose': 0, 'population_rates': None, 'init': None, 'min_samples_leaf': 1, 'criterion': 'friedman_mse', 'warm_start': False, 'loss': 'deviance', 'min_samples_split': 2}
        Environment:
         - revscoring_version: '2.3.0'
         - platform: 'Linux-4.9.0-8-amd64-x86_64-with-debian-9.5'
         - machine: 'x86_64'
         - version: '#1 SMP Debian 4.9.110-3+deb9u6 (2018-10-08)'
         - system: 'Linux'
         - processor: ''
         - python_build: ('default', 'Sep 27 2018 17:25:39')
         - python_compiler: 'GCC 6.3.0 20170516'
         - python_branch: ''
         - python_implementation: 'CPython'
         - python_revision: ''
         - python_version: '3.5.3'
         - release: '4.9.0-8-amd64'

        Statistics:
        counts (n=201261):
                label             n          ~OK    ~spam    ~vandalism    ~attack
                -----------  ------  ---  ------  -------  ------------  ---------
                'OK'         175000  -->  171425     2656           865         54
                'spam'        17699  -->    2763    14037           865         34
                'vandalism'    6503  -->    1596     1351          3213        343
                'attack'       2059  -->     252      350          1117        340
        rates:
                              'OK'    'spam'    'vandalism'    'attack'
                ----------  ------  --------  -------------  ----------
                sample       0.87      0.088          0.032       0.01
                population   0.971     0.02           0.007       0.002
        match_rate (micro=0.93, macro=0.254):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.956        0.018   0.039     0.003
        filter_rate (micro=0.07, macro=0.746):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.044        0.982   0.961     0.997
        recall (micro=0.971, macro=0.608):
                  OK    vandalism    spam    attack
                ----  -----------  ------  --------
                0.98        0.494   0.793     0.165
        !recall (micro=0.829, macro=0.946):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.824        0.985   0.976     0.998
        precision (micro=0.975, macro=0.434):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.995        0.196   0.399     0.148
        !precision (micro=0.559, macro=0.884):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.546        0.996   0.996     0.998
        f1 (micro=0.971, macro=0.489):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.987        0.281   0.531     0.156
        !f1 (micro=0.667, macro=0.908):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.657        0.991   0.986     0.998
        accuracy (micro=0.975, macro=0.981):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.975        0.982   0.973     0.996
        fpr (micro=0.171, macro=0.054):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.176        0.015   0.024     0.002
        roc_auc (micro=0.979, macro=0.971):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.979        0.956   0.979     0.968
        pr_auc (micro=0.984, macro=0.479):
                   OK    vandalism    spam    attack
                -----  -----------  ------  --------
                0.999        0.207   0.612     0.097

         - score_schema: {'title': 'Scikit learn-based classifier score with probability', 'type': 'object', 'properties': {'probability': {'type': 'object', 'description': 'A mapping of probabilities onto each of the potential output labels', 'properties': {'OK': {'type': 'number'}, 'vandalism': {'type': 'number'}, 'spam': {'type': 'number'}, 'attack': {'type': 'number'}}}, 'prediction': {'type': 'string', 'description': 'The most likely label predicted by the estimator'}}}