Random Forest in R - Classification and Prediction Example with Definition & Steps

Provides steps for applying random forest to do classification and prediction.
R code file: https://goo.gl/AP3LeZ
Data: https://goo.gl/C9emgB
Machine Learning videos: https://goo.gl/WHHqWP
GitHub: https://github.com/bkrai/Top-10-Machine-Learning-Methods-With-R
Includes,
- random forest model
- why and when it is used
- benefits & steps
- number of trees, ntree
- number of variables tried at each step, mtry
- data partitioning
- prediction and confusion matrix
- accuracy and sensitivity
- randomForest & caret packages
- bootstrap samples and out of bag (oob) error
- oob error rate
- tune random forest using mtry
- no. of nodes for the trees in the forest
- variable importance
- mean decrease accuracy & gini
- variables used
- partial dependence plot
- extract single tree from the forest
- multi-dimensional scaling plot of proximity matrix
- detailed example with cardiotocographic or ctg data

random forest is an important tool related to analyzing big data or working in data science field.

Deep Learning: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi

R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Видео Random Forest in R - Classification and Prediction Example with Definition & Steps канала Dr. Bharatendra Rai