# H2O Binomial Scorer **URL:** \ **Category:** KNIME Analytics Platform\ **Created:** [October 4, 2019, 12:29pm UTC](https://forum.knime.com/t/h2o-binomial-scorer/18627 "2019-10-04T12:29:53Z")\ **Posts on this page:** 1\ **Showing post:** 8
**Author:** ![mlauber71](https://forum-cdn.knime.com/user_avatar/forum.knime.com/mlauber71/32/73590_2.png) [@mlauber71](https://forum.knime.com/u/mlauber71)\ **Post date:** [October 4, 2019, 3:48pm UTC](https://forum.knime.com/t/h2o-binomial-scorer/18627/8 "2019-10-04T15:48:42Z")
You could use The Rule Engine and define the Threshold yourself. As @SimonS explained H2O.ai uses an optimized threshold so there is not a simple split at a score of 0.5. ![image](https://forum-cdn.knime.com/uploads/default/original/2X/1/1993836d6e9cd12400118a84b0c821b3f9ade75a.png) The best Threshold/prediction very much depends on your business model and what you want to do with the results. You could read more in these entries, especially about the ROC curve. Models for 0/1 or Yes/No Targets > [@Looking for options to evaluate a decision tree](https://forum.knime.com/t/looking-for-options-to-evaluate-a-decision-tree/11384/2): > > One ‘brutal’ way could be to convert the decision tree rules into SQL and then use the SQL code. Or use the ruleset to make some kind of If then rules. Not very elegant I have to admit. [kn\_example\_decision\_tree.knwf](https://forum-cdn.knime.com/uploads/default/original/2X/1/1132014ad9feb823a8ee8e8d5c9ce19ad8c13ba2.knwf) (510.8 KB) Understand metrics like AUC and Gini (and use H2O.ai) > [@Random Forest Model Not Working](https://forum.knime.com/t/random-forest-model-not-working/12738/3): > > I attached a working example of a Random Forest with H2O.ai nodes and a 0/1 Target from a [Kaggle competition](https://www.kaggle.com/c/bnp-paribas-cardif-claims-management/data) (please note the example only uses the first 1.000 lines, you should remove this restriction or use your own data for your own purpouses). In order to get an idea about the quality of you model you might want to read this article: I always advise not just to use a Scorer but rather a metric like Gini/AUC to see the relative quality of the model. In the end the ‘score’ is not a magic… > [@Help choosing analytics algorithm](https://forum.knime.com/t/help-choosing-analytics-algorithm/11404/3): > > I think you could do two things. You could treat your outlier group as label/target (1/0 or TRUE/FALSE) and the rest of the data as explaining variables. You would have to remove the time column since this ‘leaks’ the information you want to find. You could then use an algorithm like Random Forest Learner \*1) that also gives you a list of the most important variables, the ones that make the outliers the outlier compared to the regular cases. You could also just use the time for each transaction… --- _[View the full topic](https://forum.knime.com/t/h2o-binomial-scorer/18627)._