Model Information: - type: GradientBoosting - version: 0.8.0 - params: {'min_samples_split': 2, 'label_weights': None, 'max_depth': 7, 'min_impurity_split': None, 'learning_rate': 0.01, 'verbose': 0, 'max_features': 'log2', 'center': True, 'subsample': 1.0, 'n_estimators': 300, 'warm_start': False, 'multilabel': False, 'min_samples_leaf': 1, 'labels': ['1', '2', '3', '4', '5', '6'], 'scale': True, 'presort': 'auto', 'population_rates': None, 'loss': 'deviance', 'random_state': None, 'max_leaf_nodes': None, 'init': None, 'n_iter_no_change': None, 'criterion': 'friedman_mse', 'min_impurity_decrease': 0.0, 'validation_fraction': 0.1, 'tol': 0.0001, 'min_weight_fraction_leaf': 0.0} Environment: - revscoring_version: '2.6.9' - platform: 'Linux-4.9.0-11-amd64-x86_64-with-debian-9.12' - machine: 'x86_64' - version: '#1 SMP Debian 4.9.189-3+deb9u1 (2019-09-20)' - 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: '4.9.0-11-amd64'
Statistics:
counts (n=8921):
label n ~1 ~2 ~3 ~4 ~5 ~6
------- ---- --- ---- ---- ---- ---- ---- ----
'1' 1495 --> 1319 107 62 5 2 0
'2' 1494 --> 90 1057 300 21 21 5
'3' 1497 --> 31 94 1066 166 97 43
'4' 1489 --> 15 31 296 332 451 364
'5' 1483 --> 10 12 116 227 750 368
'6' 1463 --> 14 17 50 150 293 939
rates:
'1' '2' '3' '4' '5' '6'
---------- ----- ----- ----- ----- ----- -----
sample 0.168 0.167 0.168 0.167 0.166 0.164
population 0.712 0.188 0.055 0.033 0.006 0.007
match_rate (micro=0.494, macro=0.208):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.635 0.161 0.144 0.081 0.118 0.108
filter_rate (micro=0.506, macro=0.792):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.365 0.839 0.856 0.919 0.882 0.892
recall (micro=0.815, macro=0.612):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.882 0.707 0.712 0.223 0.506 0.642
!recall (micro=0.968, macro=0.923):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.978 0.965 0.889 0.923 0.884 0.895
precision (micro=0.878, macro=0.373):
1 2 3 4 5 6
---- ----- ---- ---- ----- -----
0.99 0.823 0.27 0.09 0.025 0.041
!precision (micro=0.822, macro=0.942):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.771 0.935 0.982 0.972 0.997 0.997
f1 (micro=0.834, macro=0.39):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.933 0.761 0.392 0.128 0.048 0.076
!f1 (micro=0.886, macro=0.929):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.862 0.949 0.933 0.947 0.937 0.944
accuracy (micro=0.909, macro=0.897):
1 2 3 4 5 6
---- ----- ----- --- ----- -----
0.91 0.917 0.879 0.9 0.882 0.894
fpr (micro=0.032, macro=0.077):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.022 0.035 0.111 0.077 0.116 0.105
roc_auc (micro=0.963, macro=0.889):
1 2 3 4 5 6
----- ----- --- ----- ----- -----
0.982 0.951 0.9 0.764 0.847 0.892
pr_auc (micro=0.9, macro=0.418):
1 2 3 4 5 6
----- ----- ----- ----- ----- -----
0.993 0.878 0.449 0.088 0.047 0.053
- score_schema: {'type': 'object', 'properties': {'probability': {'type': 'object', 'description': 'A mapping of probabilities onto each of the potential output labels', 'properties': {'3': {'type': 'number'}, '4': {'type': 'number'}, '1': {'type': 'number'}, '5': {'type': 'number'}, '2': {'type': 'number'}, '6': {'type': 'number'}}}, 'prediction': {'type': 'string', 'description': 'The most likely label predicted by the estimator'}}, 'title': 'Scikit learn-based classifier score with probability'}