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AutoML Explain: Pareto front plots in R and Python #7076
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Erin LeDell commented: [~accountid:5e43370f5a495e0c91a74ebe] I attached a prototype in R. Can be used on an AutoML object, though the final version should support H2OGrid and a list of models too (or the raw data.frame that stores the x,y,label data). Need to decide if the object should return a H2OParetoFront object (or a list) with several objects including the ggplot2 object and the data object of the Pareto front. The current prototype just returns a ggplot2 object (hence theb current _plot() name of the function), but we may want to optionally (or in addition) return the data object. {noformat}library(h2o) prostate_path <- system.file("extdata", "prostate.csv", package = "h2o") source the attached pareto code herepf <- h2o.pareto_front_plot(aml) [^h2o_pareto_front_plot.R] The details need work but it currently produces this: !Screen Shot 2022-03-05 at 6.53.05 PM.png|width=816,height=614! |
JIRA Issue Details Jira Issue: PUBDEV-8589 |
Attachments From Jira Attachment Name: h2o_pareto_front_plot.R Attachment Name: Screen Shot 2022-02-17 at 2.55.01 PM.png Attachment Name: Screen Shot 2022-03-05 at 6.53.05 PM.png |
Linked PRs from JIRA |
Add Pareto fronts (model perf vs prediction speed) for all the models in AutoML as a new “explanation” in the H2O Explain module. accuracy (auc, logloss, etc) on y-axis, predict speed on the x-axis.
{noformat}pf ← h2o.pareto_front(automl_object)
plot(pf)
pf # we need a way to return the data too{noformat}
Also since you could potentially want to make frontiers for other metrics of any group of models, so we might want to offer a way for the user to select what metrics they want on the x and y axis, and keep accuracy vs speed frontier as default for AutoML objects.
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