Introductory training

ESCAP conducted five national trainings across Asia and the Pacific as a way to introduce the LNOB platform and the LNOB methodology to policymakers and development practitioners who are interested in identifying the furthest behind and measuring inequality of opportunity. These introductory trainings offered an overview of the mathematical theory behind Classification and Regression Trees (CART) methodology as well as the Dissimilarity Index, the two components of ESCAP LNOB methodology.
The trainings also offered interactive debates over the results from each target country including Mongolia, Philippines, Tuvalu, Tonga and Turkmenistan. ESCAP is ready and willing to organize more such trainings virtually or in-person across Asia and the Pacific, especially in the context of supporting member States in their preparation for Voluntary National Reviews. Below you will find the general training materials with an in depth, step-by-step review of the LNOB methodology.

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# Table of Content Video time
1 Classification and Regression Tree (CART) Theory 0
1.1 History of CART 0
1.2 Machine Learning (CART) 0
1.3 Classification versus Regression 0
1.4 Umbrella of CART 0
1.5 Roots of our LNOB trees 0
2 ESCAP LNOB Trees 5:46
2.1 LNOB tree specifications 0
2.2 Indicators, Response Variables 0
2.3 Circumstances, Independent Variables 0
2.4 Source of data 0
2.5 LNOB Tree Example 0
2.5.1 LNOB Branches 0
2.5.2 LNOB Nodes 0
2.5.3 Furthest ahead group 0
2.5.4 LNOB Terminal nodes 0
2.5.5 Furthest behind group 0
2.6 Feature Space Diagram 0
2.6.1 All data points 0
2.6.2 1st split 0
2.6.3 2nd split 0
2.6.4 3rd split 0
2.7 LNOB Trees vs. Feature Space Diagram 0
2.8 Furthest behind group in a Feature Space Diagram 0
2.9 Furthest behind group in an LNOB Tree 0
2.10 Pros and cons of LNOB Trees 0
3 Dissimilarity Index 0
3.1 Origins of D-Index 0
3.2 D-index Scale 0
3.3 Video on D-Index 4:26
3.4 D-Index formula 0
3.4.1 Number of groups 0
3.4.2 Calculating number of groups 0
3.4.3 Country average access rate 0
3.4.4 Group access rate 0
3.4.5 Weighted sampling of group 0
3.4.6 Example of calculating D-Index 0
3.5 D-Index and barriers 0