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H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
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Fix misleading Confusion Matrix header for binomial models in R and Python #12763

Open exalate-issue-sync[bot] opened 1 year ago

exalate-issue-sync[bot] commented 1 year ago

A while back we [fixed|https://0xdata.atlassian.net/browse/PUBDEV-4250] the misleading confusion matrix header (people thought the rows/cols were reversed), but the update only worked for multinomial models (tested only on iris data). We need to update this for binomial models too.

exalate-issue-sync[bot] commented 1 year ago

Sarang Deshpande commented: While we are on this, it might be a good idea to rename the last column header in confusion matrix, i.e. "Rate" to be more specific i.e. "Error Rate".

hasithjp commented 1 year ago

JIRA Issue Migration Info

Jira Issue: PUBDEV-5911 Assignee: Erin LeDell Reporter: Erin LeDell State: Open Fix Version: N/A Attachments: N/A Development PRs: N/A

hutch3232 commented 1 year ago

+1 on this issue. I was tripped up by it until I found this post: https://stackoverflow.com/questions/44315497/how-to-interpret-h2os-confusion-matrix Was going to make an issue until I found this one already existed.