## 3 Result and Discussion
### LIME Explanation  
The LIME plots offer a comprehensive explanation of the model's predictions on whether a child has learning difficulties based on various input features. These explanations, which were generated using the best-performing XGB model, emphasize the critical role of factors such as health, economic conditions, and parenting practices in predicting learning difficulties. They also identify areas that require improvement, especially in addressing factors that adversely affect a child's learning abilities.  
In Figure 13, the model predicts a 0.93 probability that the child does not have learning difficulties. The primary factors contributing to this prediction are as follows:  
* The factors of child's weight (14.30) and weight-age percentile (37.50) significantly influence the prediction that the child does not have learning difficulties. Higher values indicate good physical health, which is strongly associated with better cognitive development and learning capabilities;
* A higher wealth index (Richest = 5), which often provides access to better resources and educational opportunities, contributes to a lower likelihood of learning difficulties. This positive socio-economic status helps to minimize the negative impact of removing privileges (YES = 1), which otherwise increases the risk of learning difficulties;
* A lower level of maternal education (Primary = 1) is often associated with a higher likelihood of experiencing learning difficulties, which has some impact on the prediction;
* Negative parenting behaviors, such as shaking the child (YES = 1), increase the likelihood of learning difficulties. But beating hard (NO = 2) and calling the child dumb/lazy (NO = 2) indicate that the absence of severe physical punishment and abuse decreases the likelihood of learning difficulties;
* The factor Sex (FEMALE = 2) contributes to the prediction that female children have a lower probability of experiencing learning difficulties.  
Figure 12: SHAP waterfall plot for predicting the absence of learning difficulties in a child.  
Furthermore, in Figure 14, the model predicts with a high probability (1.00) that the child has learning difficulties. The key contributing factors to this prediction are as follows:  
* The factors of the child's weight (11.28) and weight-age percentile (1.30) significantly contribute to the prediction of learning difficulties because the child's low weight and weight-for-age percentile suggest poor health, which is often associated with developmental challenges;
* The factors like removing privileges (YES = 1) along with wealth index (Poorest = 1) conditions are linked to a higher likelihood of learning difficulties;
* The mother's education (Primary = 1) factor slightly influences the prediction, as a lower level of maternal education is often associated with an increased risk of learning difficulties;
* The factor sex (FEMALE = 2) contributes to the \"No difficulties\" prediction in this case;
* Negative parenting behaviors: Shaking the child (NO = 2) and beating hard (NO = 2) slightly decreases the likelihood of learning difficulties, but calling the child dumb/lazy (YES = 1) contributes to the prediction of have difficulties, reinforcing the model's overall prediction.  
Figure 14: LIME plot explaining prediction of the presence of learning difficulties in a child.  
Figure 13: LIME plot explaining prediction of the absence of learning difficulties in a child.