#	Table of Content
1	Classification and Regression Tree (CART) Theory
1.1	History of CART
1.2	Machine Learning (CART)
1.3	Classification versus Regression
1.4	Umbrella of CART
1.5	Roots of our LNOB trees
2	ESCAP LNOB Trees
2.1	LNOB tree specifications
2.2	Indicators, Response Variables
2.3	Circumstances, Independent Variables
2.4	Source of data
2.5	LNOB Tree Example
2.5.1	LNOB Branches
2.5.2	LNOB Nodes
2.5.3	Furthest ahead group
2.5.4	LNOB Terminal nodes
2.5.5	Furthest behind group
2.6	Feature Space Diagram
2.6.1	All data points
2.6.2	1st split
2.6.3	2nd split
2.6.4	3rd split
2.7	LNOB Trees vs. Feature Space Diagram
2.8	Furthest behind group in a Feature Space Diagram
2.9	Furthest behind group in an LNOB Tree
2.10	Pros and cons of LNOB Trees
3	Dissimilarity Index
3.1	Origins of D-Index
3.2	D-index Scale
3.3	Video on D-Index
3.4	D-Index formula
3.4.1	Number of groups
3.4.2	Calculating number of groups
3.4.3	Country average access rate
3.4.4	Group access rate
3.4.5	Weighted sampling of group
3.4.6	Example of calculating D-Index
3.5	D-Index and barriers

Weighted sampling of group

B_i is the weighted sampling proportion of group I, where the some of B_i should equal 1.

Introductory training