Which node allows you to select a partition for model evaluation?

The Select node is integral to model evaluation as it allows users to choose specific partitions of the dataset that can be used for training and testing their predictive models. This capability is crucial for ensuring that the evaluation reflects the model's performance on unseen data.

By using the Select node, one can effectively manage how much of the data is utilized for model training and how much is designated for testing or validation. This selective partitioning is key to achieving accurate model evaluation metrics and understanding the model's generalization to new data.

In contrast, the other options serve different purposes. The Distribution node primarily helps in visualizing the distribution of variables, which is not directly related to partitioning data for model evaluation. The Filter node is used to include or exclude specific data records based on certain criteria but does not focus on selecting specific partitions for model evaluation. Lastly, the Matrix node is typically employed for cross-tabulation and comparison of different variables, rather than partition selection for modeling purposes.