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For prediction the speedup is around 2-3x for varying number of components and features. For the number of samples the cross-over point is around O(10^4) samples.
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For prediction the speedup is around 5-6x for varying number of components and features and ~50x speedup on the GPU. For the number of samples the cross-over point is around O(10^3) samples.
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### Training Time
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| Time vs. Number of Components | Time vs. Number of Samples | Time vs. Number of Features |
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For training the speedup is around >10x on the same architecture and close to 100x speedup on the GPU. However there is no guarantee that it will converge to the same solution as Scikit-Learn. But there are some tests in the `tests` folder that compare the results of the two implementations which shows good agreement.
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For training the speedup is around ~5-6x on the same architecture and ~50x speedup on the GPU. However there is no guarantee that it will converge to the same solution as Scikit-Learn. But there are some tests in the `tests` folder that compare the results of the two implementations which shows good agreement.
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