⭐️ Add entity embeddings workflow example #278
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This seems to be the source of the test fail with the error being That said, I believe I have only modified the docs folder so that should be completely orthogonal on this fail. |
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ablaom
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Nov 13, 2024
| # It employs a set of embedding layers to map each categorical feature into a dense continuous vector in a similar fashion to how they are employed in NLP architectures. | ||
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| # In MLJFlux, the `NeuralNetworkClassifier`, `NeuralNetworkRegressor`, and the `MultitargetNeuralNetworkRegressor`` can be trained and evaluated with heterogenous data (i.e., containing categorical features) because they have a built-in entity embedding layer. | ||
| # Moreover, they now offer a transform which encode the categorical features with the learnt embeddings to be used by an upstream machine learning model. |
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| # Moreover, they now offer a transform which encode the categorical features with the learnt embeddings to be used by an upstream machine learning model. | |
| # Moreover, they now offer a `transform` method which encodes the categorical features with the learned embeddings to be used by an upstream machine learning model. |
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I'm a little confused by what is meant by "to be used by an upstream machine learning model".
ablaom
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Nov 13, 2024
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It dawned on me that entity embeddings is a noteworthy feature of this package and that it deserves a workflow example illustrating: (i), how it can be used and (ii), hints on how it works.
In particular, I was going to proceed with making an interface at MLJTransforms but felt I need to refresh my self on how this works first so I thought making this tutorial is a good way.