White Papers
Utilizing CART Machine-Learning Models in Early Childhood Development (ECD)

Utilizing CART Machine-Learning Models to Explore the Intersections of Determinants of Key Early Childhood Development (ECD) Outcomes

Early childhood is a critical period that affects the holistic development of an individual and determines their ability to reach their optimum health, social, and economic potential. Determinants of early childhood development, such as wealth, the child's gender, education levels of caregivers, violent practices against children, inadequate care of children at home, and the mental health of the primary caregiver, have the most significant impact on a child's development.

In this white paper, we share our experience of applying machine-learning models associated with the Classification and Regression Tree (CART) algorithm in exploring determinants of key early childhood development outcomes from an end-line evaluation Athena Infonomics conducted for UNICEF Rwanda.

Image © UNICEF/UN0329475/Kanobana

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