Two cutting edge techniques : - SmartPracticeschool/llSPS-INT-3838-Employee-Attrition-Prediction GitHub Wiki

1. Machine Learning with h2o.automl() from the h2o package: This function takes automated machine learning to the next level by testing a number of advanced algorithms such as random forests, ensemble methods, and deep learning along with more traditional algorithms such as logistic regression. The main takeaway is that we can now easily achieve predictive performance that is in the same ball park (and in some cases even better than) commercial algorithms and ML/AI software.

2. Feature Importance with the lime package:

The problem with advanced machine learning algorithms such as deep learning is that it’s near impossible to understand the algorithm because of its complexity. This has all changed with the lime package. The major advancement with lime is that, by recursively analyzing the models locally, it can extract feature importance that repeats globally. What this means to us is that lime has opened the door to understanding the ML models regardless of complexity. Now the best (and typically very complex) models can also be investigated and potentially understood as to what variables or features make the model tick.