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.
I see that h2o implements Naive Bayes. Nevertheless, it only assumes gaussian distribution for contiuous covariates. The klaR R package implements kernel density estimation for continuous covariate that oftenly significantly improve predictive performance. Are there any plans to add this feature in h2o implementation?
I see that h2o implements Naive Bayes. Nevertheless, it only assumes gaussian distribution for contiuous covariates. The klaR R package implements kernel density estimation for continuous covariate that oftenly significantly improve predictive performance. Are there any plans to add this feature in h2o implementation?
Source: https://community.h2o.ai/questions/1376/suggestion-for-naive-bayes.html