Model Information:
	 - type: GradientBoosting
	 - version: 0.4.0
	 - params: {'max_depth': 3, 'labels': [True, False], 'warm_start': False, 'max_leaf_nodes': None, 'init': None, 'learning_rate': 0.1, 'verbose': 0, 'population_rates': None, 'subsample': 1.0, 'min_weight_fraction_leaf': 0.0, 'scale': True, 'presort': 'auto', 'label_weights': {True: 10}, 'loss': 'deviance', 'center': True, 'random_state': None, 'min_samples_leaf': 1, 'n_estimators': 300, 'max_features': 'log2', 'min_samples_split': 2}
	Environment:
	 - revscoring_version: '2.0.6'
	 - platform: 'Linux-4.4.0-3-amd64-x86_64-with-debian-8.7'
	 - machine: 'x86_64'
	 - version: '#1 SMP Debian 4.4.2-3+wmf8 (2016-12-22)'
	 - system: 'Linux'
	 - processor: ''
	 - python_build: ('default', 'Oct  8 2014 10:45:20')
	 - python_compiler: 'GCC 4.9.1'
	 - python_branch: ''
	 - python_implementation: 'CPython'
	 - python_revision: ''
	 - python_version: '3.4.2'
	 - release: '4.4.0-3-amd64'
	
	Statistics:
	counts (n=19617):
		label        n         ~True    ~False
		-------  -----  ---  -------  --------
		True      2498  -->     2263       235
		False    17119  -->     3923     13196
	rates:
		              True    False
		----------  ------  -------
		sample       0.127    0.873
		population   0.11     0.89
	match_rate (micro=0.653, macro=0.5):
		  False    True
		-------  ------
		  0.696   0.304
	filter_rate (micro=0.347, macro=0.5):
		  False    True
		-------  ------
		  0.304   0.696
	recall (micro=0.786, macro=0.838):
		  False    True
		-------  ------
		  0.771   0.906
	!recall (micro=0.891, macro=0.838):
		  False    True
		-------  ------
		  0.906   0.771
	precision (micro=0.913, macro=0.657):
		  False    True
		-------  ------
		  0.985   0.329
	!precision (micro=0.401, macro=0.657):
		  False    True
		-------  ------
		  0.329   0.985
	f1 (micro=0.823, macro=0.674):
		  False    True
		-------  ------
		  0.865   0.483
	!f1 (micro=0.525, macro=0.674):
		  False    True
		-------  ------
		  0.483   0.865
	accuracy (micro=0.786, macro=0.786):
		  False    True
		-------  ------
		  0.786   0.786
	fpr (micro=0.109, macro=0.162):
		  False    True
		-------  ------
		  0.094   0.229
	roc_auc (micro=0.921, macro=0.922):
		  False    True
		-------  ------
		  0.921   0.922
	pr_auc (micro=0.952, macro=0.82):
		  False    True
		-------  ------
		   0.99    0.65