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We should make an example that does model selection with a pipeline that includes a TableVectorizer, to show the benefit of tuning jointly the preprocessing and the learner.
However, doing this example may reveal shortcomings in our design of the TableVectorizer. For instance, I realized that I cannot use and RandomSearchCV to set the dimensionality of the GapEncoder (because this dimensionality is not a parameter accessible at the level of the TableVectorizer). We might need to brainstorm a bit more.
The text was updated successfully, but these errors were encountered:
We should make an example that does model selection with a pipeline that includes a TableVectorizer, to show the benefit of tuning jointly the preprocessing and the learner.
However, doing this example may reveal shortcomings in our design of the TableVectorizer. For instance, I realized that I cannot use and RandomSearchCV to set the dimensionality of the GapEncoder (because this dimensionality is not a parameter accessible at the level of the TableVectorizer). We might need to brainstorm a bit more.
The text was updated successfully, but these errors were encountered: