### Instruction ###
Your task is to provide a detailed explanation of how gradient-boosting regression trees can be applied to survey sampling and small area estimation, with an emphasis on practical implementation and accuracy estimation. You MUST address the following points:
1. Elaborate on the gradient-boosting regression tree methodology, emphasizing its suitability for modeling complex relationships in survey sampling and small area estimation data.
2. Highlight the robustness of machine learning methods, particularly gradient-boosting regression trees, against small deviations from traditional statistical model assumptions.
3. Clarify the role of bootstrap techniques, including parametric and residual bootstrap methods, in quantifying the accuracy and reliability of machine learning model predictions.
4. Compare the performance of machine learning predictors with classical statistical methods, both under ideal conditions and when faced with model deviations.
5. Discuss the practical steps involved in implementing gradient-boosting regression trees, such as data preprocessing, model tuning, and interpretation of bootstrap accuracy estimates, while considering the potential limitations and the need for domain expertise.
6. Provide guidance on selecting appropriate machine learning models and evaluating their predictions within the context of survey sampling and small area estimation.
Ensure that your explanation is clear, factual, and tailored for an audience with an intermediate to advanced understanding of statistics and machine learning, enabling them to apply these insights effectively in their work.