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drop some fluff from docs/index.md
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@@ -92,16 +92,6 @@ then overloading `obs` is completely optional. Plain iteration interfaces, with
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knowledge of the number of observations, can also be specified (to support, e.g., data
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loaders reading images from disk).
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## Hooks adding algorithm-generic functonality
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Given any training `data` consumed by `fit` (such as `data = (X, y)` in the example above)
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[`LearnAPI.features(algorithm, data)`](@ref input) tells us what part of `data` comprises
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*features*, which is something that can be passed onto to `predict` or `transform` (`X` in
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the example) while [`LearnAPI.target(algorithm, data)`](@ref), if implemented, tells us
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what part comprises the target (`y` in the example). By explicitly requiring such methods,
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LearnAPI.jl frees algorithms to consume data in multiple forms, including optimised,
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algorithm-specific forms, as described above.
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## Learning more
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- [Anatomy of an Implementation](@ref): informal introduction to the main actors in a new

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