Replication Data for: Tree-Based Models for Political Science Data 
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Political scientists often find themselves analyzing datasets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in ``big data'' as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high-dimensional datasets. In this paper, we introduce a family of tree-based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in datasets with many (potentially irrelevant) covariates. We introduce the basic logic of tree-based models, provide an overview of the most prominent methods in the li' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Classification and regression trees"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Political scientists often find themselves analyzing datasets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in ``big data'' as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high-dimensional datasets. In this paper, we introduce a family of tree-based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in datasets with many (potentially irrelevant) covariates. We introduce the basic logic of tree-based models, provide an overview of the most prominent methods in the li' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Political scientists often find themselves analyzing datasets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in ``big data'' as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high-dimensional datasets. In this paper, we introduce a family of tree-based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in datasets with many (potentially irrelevant) covariates. We introduce the basic logic of tree-based models, provide an overview of the most prominent methods in the li
Classification and regression trees