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

Описание: Provides steps for applying random forest to do classification and prediction.
Research article on random forest: https://www.igi-global.com/pdf.aspx?t...

Data: https://github.com/bkrai/R-files-from...
Machine Learning videos: https://goo.gl/WHHqWP
R code: https://github.com/bkrai/Top-10-Machi...

For citation as reference in a research paper, use following:
Meshram, A., and Rai, B. (2019). “User-Independent Detection for Freezing of Gait in Parkinson’s Disease Using Random Forest Classification,” International Journal of Big Data and Analytics in Healthcare, Vol. 4, Issue 1, 57-72.

Rai BK (2017) “Feature Selection and Predictive Modeling of Housing Data Using Random Forest,” International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering, Vol. 11, No. 4, 880-884.

Xiaoling, Lu., Rai, B., Yan, Z., and Li, Y. (2018). “Cluster-based Smartphone Predictive Analytics for Application Usage and Next Location Prediction,” International Journal of Business Intelligence Research, Vol. 9, No. 2, 64-80.

Rai BK, (2020). “Supervised Machine Learning: Application Example Using Random Forest in R,” chapter in book titled Mathematics Applied to Engineering and Management, edited by Mangey Ram and S. B. Singh, CRC Press Taylor & Francis Company.

Topics
00:00 CTG data description
01:58 Data partition
03:04 What is a random forest classification model? How it work? Why and when to use?
08:16 Random forest in R
10:51 Prediction & confusion matrix - train data, caret package, accuracy, sensitivity & interpretation
16:27 Prediction and confusion matrix with test data
17:33 Error rate of random forest, bootstrap samples and out of bag (oob) error
18:04 Tune random forest model
22:25 Number of nodes for trees
23:33 Variable importance
27:04 Partial dependence plot
28:31 Extract single tree from the forest
29:38 Multi-dimensional scaling plot of proximity matrix

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

Machine Learning videos: https://goo.gl/WHHqWP
Becoming Data Scientist: https://goo.gl/JWyyQc
Introductory R Videos: https://goo.gl/NZ55SJ
Deep Learning with TensorFlow: https://goo.gl/5VtSuC
Image Analysis & Classification: https://goo.gl/Md3fMi
Text mining: https://goo.gl/7FJGmd
Data Visualization: https://goo.gl/Q7Q2A8
Playlist: https://goo.gl/iwbhnE

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.

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