Research talk:Revision scoring as a service/Work log/2016-02-10

        revscoring train_test \
                revscoring.scorer_models.RF \
                wb_vandalism.feature_lists.experimental.general_context_and_type \
                --version 0.0.1 \
                -p 'max_features="log2"' \
                -p 'criterion="entropy"' \
                -p 'min_samples_leaf=1' \
                -p 'n_estimators=80' \
                -s 'pr' -s 'roc' \
                -s 'recall_at_fpr(max_fpr=0.10)' \
                -s 'filter_rate_at_recall(min_recall=0.90)' \
                -s 'filter_rate_at_recall(min_recall=0.75)' \
                --balance-sample-weight \
                --center --scale \
                --label-type=bool > \
        models/models/wikidata.reverted.general_context_and_type.rf.model
2016-02-10 19:30:22,572 INFO:revscoring.utilities.train_test -- Training model...
2016-02-10 19:31:49,619 INFO:revscoring.utilities.train_test -- Testing model...
ScikitLearnClassifier
 - type: RF
 - params: max_features="log2", max_leaf_nodes=null, class_weight=null, min_samples_leaf=1, center=true, n_estimators=80, min_samples_split=2, balanced_sample_weight=true, bootstrap=true, n_jobs=1, scale=true, oob_score=false, warm_start=false, max_depth=null, min_weight_fraction_leaf=0.0, random_state=null, criterion="entropy", verbose=0
 - version: 0.0.1
 - trained: 2016-02-10T19:31:49.613717

         ~False    ~True
-----  --------  -------
False     98446      662
True        139        8

Accuracy: 0.9919298775880309

Filter rate @ 0.9 recall: threshold=0.0, filter_rate=0.0, recall=1.0
Recall @ 0.1 false-positive rate: threshold=None, recall=None, fpr=None
Filter rate @ 0.75 recall: threshold=0.0, filter_rate=0.0, recall=1.0
ROC-AUC: 0.8
PR-AUC: 0.019