Dmitriy, Sure, all changes in ML module will be described on readme.io site with next release (2.8).
Best regards, Yuriy Babak чт, 13 дек. 2018 г. в 17:21, Dmitriy Pavlov <dpav...@apache.org>: > Folks, I sometimes hear complains related to metrics and its clearness for > end-users. > > Would you add a couple of words related to each value to wiki/readme.io? > > чт, 13 дек. 2018 г. в 17:13, Alexey Zinoviev <zaleslaw....@gmail.com>: > > > So, I agree that we should avoid ineffective metrics calculations. > > I think that in 2.8 release we should have > > > > 1. BinaryClassificationMetric with all metrics from Wikipedia > > 2. Metric interface with 1 or two implementations in example folder or > > in metric package like roc auc and accuracy > > 3. BinaryClassificationMetric and MultiClassClassificationMetrics > should > > implement new interface MetricGroup > > > > Will totally change the current PR according your recommendation > > > > чт, 13 дек. 2018 г. в 16:06, Алексей Платонов <aplaton...@gmail.com>: > > > > > You can compute just TP (true-positive), FP, TN and FN counters and use > > > them to evaluate Recall, Precision, Accuracy, ect. If you want to > specify > > > class for Pr evaluation, then you can compute Pr for first label as > > > TP/(TP+FP) and for second label as TN/(TN+FN) for example. After it we > > can > > > unite all one-point metrics evaluation. > > > > > > In my opinion we can redesign metrics calculation and provide one-point > > > metrics (like Pr, Re) and integral metrics like ROC AUC where one-point > > > metrics can be calculated through TP,FP etc. > > > > > > Maybe you should design class BinaryClassificationMetric that computes > > > these counters and provide methods like recall :: () -> double, > precision > > > :: () -> double, etc. > > > > > > чт, 13 дек. 2018 г. в 13:26, Yuriy Babak <y.ch...@gmail.com>: > > > > > > > Igniters, Alexey > > > > > > > > I want to discuss the ticket 10371 [1], currently, we calculate 4 > > numbers > > > > (true positive, true negative, false positive, false negative) for > each > > > > "point metric" like accuracy, recall, f-score and precision for each > > > label. > > > > > > > > So for the full score we need calculates those 4 numbers 8 times. But > > we > > > > could calculate all 8 metrics(4 for the first label and 4 for the > > second > > > > label). > > > > > > > > I suggest introducing new API "point metric" for metrics like those > > > > 4(accuracy, recall, f-score, and precision) and "integral metric" for > > > > metrics like ROC AUC [2]. > > > > > > > > Any thoughts would be appreciated. > > > > > > > > [1] - https://issues.apache.org/jira/browse/IGNITE-10371 > > > > [2] - https://issues.apache.org/jira/browse/IGNITE-10145 > > > > > > > > > >
