Machine Learning and Deep learning in R for air quality - PM2.5 and Aerosol optical depth data.

This exercise is implemented using the open source H2O platform.

Paper

Random Forest

• https://uc-r.github.io/random_forests

• https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/drf.html

Neural Network

• https://shiring.github.io/machine_learning/2017/03/07/grid_search

• https://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/deep-learning.html

• https://www.hackerearth.com/practice/machine-learning/machine-learning-algorithms/understanding-deep-learning-parameter-tuning-with-mxnet-h2o-package-in-r/tutorial/

• https://htmlpreview.github.io/?https://github.com/ledell/sldm4-h2o/blob/master/sldm4-deeplearning-h2o.html

LightGBM

• https://www.kaggle.com/andrewmvd/lightgbm-in-r/report

• https://neptune.ai/blog/lightgbm-parameters-guide

• https://www.r-bloggers.com/2020/08/how-to-use-lightgbm-with-tidymodels/

Extras

• Example - https://www.mdpi.com/2072-4292/13/18/3790

• Cross Validation - https://docs.h2o.ai/h2o/latest-stable/h2o-docs/cross-validation.html#combining-holdout-predictions

• Random Grid Search - https://docs.h2o.ai/h2o/latest-stable/h2o-docs/grid-search.html

• Variable Importance - https://docs.h2o.ai/h2o/latest-stable/h2o-docs/variable-importance.html