Model Information:
	 - type: GradientBoosting
	 - version: 0.6.0
	 - params: {'presort': 'auto', 'population_rates': None, 'learning_rate': 0.01, 'min_weight_fraction_leaf': 0.0, 'loss': 'deviance', 'max_depth': 7, 'labels': ['Stub', 'Start', 'C', 'B', 'GA', 'FA'], 'verbose': 0, 'init': None, 'min_samples_leaf': 1, 'max_leaf_nodes': None, 'center': True, 'scale': True, 'random_state': None, 'multilabel': False, 'n_estimators': 700, 'label_weights': None, 'subsample': 1.0, 'max_features': 'log2', 'warm_start': False, 'min_samples_split': 2}
	Environment:
	 - revscoring_version: '2.1.0'
	 - platform: 'Linux-4.9.0-6-amd64-x86_64-with-debian-9.4'
	 - machine: 'x86_64'
	 - version: '#1 SMP Debian 4.9.82-1+deb9u3 (2018-03-02)'
	 - system: 'Linux'
	 - processor: ''
	 - python_build: ('default', 'Jan 19 2017 14:11:04')
	 - python_compiler: 'GCC 6.3.0 20170118'
	 - python_branch: ''
	 - python_implementation: 'CPython'
	 - python_revision: ''
	 - python_version: '3.5.3'
	 - release: '4.9.0-6-amd64'
	
	Statistics:
	counts (n=665):
		label      n         ~Stub    ~Start    ~C    ~B    ~GA    ~FA
		-------  ---  ---  -------  --------  ----  ----  -----  -----
		'Stub'    24  -->       14         3     0     0      6      1
		'Start'   22  -->        4         2     3     6      7      0
		'C'       26  -->        1         1     2     1     18      3
		'B'       66  -->        1         0     1     9     41     14
		'GA'     273  -->        0         0     1    10    189     73
		'FA'     254  -->        1         1     0     6     80    166
	rates:
		              'Stub'    'Start'    'C'    'B'    'GA'    'FA'
		----------  --------  ---------  -----  -----  ------  ------
		sample         0.036      0.033  0.039  0.099   0.411   0.382
		population     0.036      0.033  0.039  0.099   0.411   0.382
	match_rate (micro=0.365, macro=0.167):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.032  0.048  0.386  0.513  0.011    0.011
	filter_rate (micro=0.635, macro=0.833):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.968  0.952  0.614  0.487  0.989    0.989
	recall (micro=0.574, macro=0.372):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.583  0.136  0.654  0.692  0.077    0.091
	!recall (micro=0.751, macro=0.888):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.989  0.962  0.779  0.612  0.992    0.992
	precision (micro=0.547, macro=0.453):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.667  0.281  0.646  0.554  0.286    0.286
	!precision (micro=0.799, macro=0.892):
		  Stub     B     FA     GA      C    Start
		------  ----  -----  -----  -----  -------
		 0.984  0.91  0.784  0.741  0.964     0.97
	f1 (micro=0.551, macro=0.388):
		  Stub      B    FA     GA      C    Start
		------  -----  ----  -----  -----  -------
		 0.622  0.184  0.65  0.616  0.121    0.138
	!f1 (micro=0.773, macro=0.889):
		  Stub      B     FA    GA      C    Start
		------  -----  -----  ----  -----  -------
		 0.987  0.935  0.781  0.67  0.978    0.981
	accuracy (micro=0.736, macro=0.858):
		  Stub     B     FA     GA      C    Start
		------  ----  -----  -----  -----  -------
		 0.974  0.88  0.731  0.645  0.956    0.962
	fpr (micro=0.249, macro=0.112):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.011  0.038  0.221  0.388  0.008    0.008
	roc_auc (micro=0.735, macro=0.806):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.987  0.663  0.749  0.694  0.811    0.934
	pr_auc (micro=0.537, macro=0.414):
		  Stub      B     FA     GA      C    Start
		------  -----  -----  -----  -----  -------
		 0.674  0.205  0.647  0.563  0.147    0.249
	
	 - score_schema: {'type': 'object', 'title': 'Scikit learn-based classifier score with probability', 'properties': {'probability': {'type': 'object', 'description': 'A mapping of probabilities onto each of the potential output labels', 'properties': {'Stub': 'number', 'B': 'number', 'FA': 'number', 'GA': 'number', 'C': 'number', 'Start': 'number'}}, 'prediction': {'type': 'string', 'description': 'The most likely label predicted by the estimator'}}}