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.
Previous thoughts are:
There are two sets of parameters to set for each infogram mode: for fairness or for core.
Hence, I set the parameters to -1 to indicate that users have not set more parameters than they should.
However, thinking back here, it is more important to set the parameters to the correct values than reprimanding them if they set more parameters than they should. Hence, I just go in and set the parameters to their default values instead of -1.
Here is the issue: https://github.com/h2oai/h2o-3/issues/16420
Previous thoughts are: There are two sets of parameters to set for each infogram mode: for fairness or for core.
Hence, I set the parameters to -1 to indicate that users have not set more parameters than they should.
However, thinking back here, it is more important to set the parameters to the correct values than reprimanding them if they set more parameters than they should. Hence, I just go in and set the parameters to their default values instead of -1.