Why You Should Build XGBoost Models Within H2O

XGBoost triggered the rise of the tree based models in the machine learning world. It earns reputation with its robust … More

A Gentle Introduction to XGBoost for Applied Machine Learning

XGBoost is firstly introduced in 2016 by Washington University Professors Tianqi Chen and Carlos Guestrin. Even though XGBoost appears in an … More

Artistic Style Transfer for Videos

State-of-the-art states the highest level in English. Because art is outpouring of human intelligence and sense of aesthetics. Artistic style transfer is … More

Mish As Neural Networks Activation Function

Recently, Mish activation function is announced in deep learning world. Researchers report that it overperforms than both regular ReLU and Swish. The … More

Interpretable Machine Learning with H2O and SHAP

Previously, we’ve made explanations for h2o.ai models with lime. Lime enables questioning for made predictions of built models. Herein, SHAP … More

Explaining h2o models with Lime

Interpretability and accuracy inversely proportional concepts. Models offering higher accuracy such as deep learning or GBM would be lowly interpretable. … More