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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -33,9 +33,9 @@ Training control, Losses/metrics, Multioutput handling strategy, Anything via cu

## SketchBoost [paper](https://openreview.net/forum?id=WSxarC8t-T)

**Multioutput training**. Current state-of-atr boosting toolkits provide very limited support of multioutput training.
And even if this option is available, training time for such tasks as multiclass/multilabel classification and multitask
regression is quite slow because of the training complexity that scales linearly with the number of outputs. To overcome
**Multioutput training**. Current state-of-the-art boosting toolkits offer limited support for multi-output training.
Even when this option is available, training times for tasks such as multiclass/multilabel classification and multitask
regression are quite slow due to the training complexity, which scales linearly with the number of outputs. To overcome
the existing limitations we create **SketchBoost** algorithm that uses approximate tree structure search. As we show
in [paper](https://openreview.net/forum?id=WSxarC8t-T) that strategy at least does not lead to performance decrease and
often is able to improve the accuracy
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