A collection of statistics generators that can be applied to revscoring.ScorerModel.
Constructs an accuracy generator.
When applied to a test set, the score() method will return a float representing the proportion of correct predicitions.
Constructs an accuracy generator.
When applied to a test set, the score() method will return a float representing the proportion of correct predicitions.
Constructs an accuracy generator.
When applied to a test set, the score() method will return a float representing the proportion of correct predicitions.
Constructs a reciever operating characteristic statistics generator. See https://en.wikipedia.org/wiki/Receiver_operating_characteristic
When applied to a test set, the score() method will return a dictionary with four fields:
 auc: the area under the ROC curve
 fprs: a list of falsepositive rates
 tprs: a list of truepositive rates
 thresholds: a list of probability thresholds
Constructs a precision/recall statistics generator. See https://en.wikipedia.org/wiki/Precision_and_recall
When applied to a test set, the score() method will return a dictionary with four fields:
 auc: the area under the precisionrecall curve
 precisions: a list of precisions
 recalls: a list of recalls
 thresholds: a list of probability thresholds
Constructs a statistics generator that measures the maximum recall that can be achieved at maximum falsepositive rate. As a classifier gets better, the attainable recall at low falsepositive rates should increase.
When applied to a test set, the score() method will return a dictionary with three fields:
 threshold: The probability threshold where recall was maximized
 recall: The recall at threshold
 fpr: The falsepositive rate at threshold
Parameters: 


Constructs a statistics generator that measures the maximum filter rate that can be achieved at minum recalle. As a classifier gets better, the attainable filter rate at high recall values should go up.
When applied to a test set, the score() method will return a dictionary with three fields:
 threshold: The probability threshold where filter rate was maximized
 filter_rate: The filter rate at threshold
 recall: The recall at threshold
Parameters: 

