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Fix sentence and add MLJ citation
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EssamWisam authored Mar 17, 2024
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Expand Up @@ -96,7 +96,7 @@ Methods implemented in the `Imbalance.jl` toolbox indeed meet all aforementioned
- Should expose a pure functional implementation, but also support popular Julia machine learning interfaces
- Should be possible to wrap an arbitrary number of resampler models with a classification model to behave as a unified model

Methods implemented in the `Imbalance.jl` toolbox meet all the interface design principles above. It particularly implements the `MLJ` and `TableTransforms` interface for each method. `BalancedModel` from `MLJBalancing.jl` also allows fusing an arbitrary number of resampling models and a classifier together to behave as one unified model.
Methods implemented in the `Imbalance.jl` toolbox meet all the interface design principles above. It particularly implements the `MLJ` [@Blaom2020MLJAJ] and `TableTransforms` interface for each method. `BalancedModel` from `MLJBalancing.jl` also allows fusing an arbitrary number of resampling models and a classifier together to behave as one unified model.


### Developer Experience and Testing
Expand All @@ -108,7 +108,7 @@ Methods implemented in the `Imbalance.jl` toolbox meet all the interface design
- Should document all functions, including internal ones
- Comments should be included to justify or simplify written implementations when needed

This set of design principles is also satisfied by `Imbalance.jl`. Implemented techniques are tested by testing smaller units that form the technique. Aside from that, end-to-end tests are performed for each technique by testing properties and characteristics of the technique or by using the `imbalanced-learn` toolbox from Python and comparing outputs.
This set of design principles is also satisfied by `Imbalance.jl`. Implemented techniques are tested by testing smaller units that form them. Aside from that, end-to-end tests are performed for each technique by testing properties and characteristics of the technique or by using the `imbalanced-learn` toolbox from Python and comparing outputs.

### User Experience

Expand All @@ -118,7 +118,7 @@ This set of design principles is also satisfied by `Imbalance.jl`. Implemented t
- A practical example that uses the method with real data should preferably accompany each method
- If an implemented method lacks an online explanation, an article that explains the method after it is implemented should be preferably written

The `Imbalance.jl` documentation indeed satisfies this set of design principles. Methods are associated with examples that can be copy-pasted, examples that demonstrate the operation of the technique visually, and possibly, examples that use it with a real-world dataset to improve the performance of a classification model.
The `Imbalance.jl` documentation indeed satisfies this set of design principles. Methods are each associated with an example that can be copy-pasted, a visual example that demonstrates the operation of the technique, and possibly, an example that utilizes it with a real-world dataset to improve the performance of a classification model.



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