Added more options like min_samples_split,min_samples_leaf,max_features and max_nodes in Decision Tree scikit implementation - #43
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Can one of the admins verify this patch? |
zoq
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Apr 5, 2017
| random_state=self.seed) | ||
| random_state=self.seed, | ||
| splitter = self.splitter, | ||
| min_samples_split = self.min_samples_split, min_weight_fraction_leaf=self.min_weight_fraction_leaf, |
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Can you use one line for each parameter, to improve the readability?
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Done. Please Review. |
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@mlpack-jenkins test this |
Contributor
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Not getting why the benchmark_ann file fails to compile here. Last time it was checked it compiled just fine. |
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It compiles without any warnings on my local system while working on the same branch. |
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@Iron-Stark Turns out the test sometimes fails when used with a small dataset, I'm not why. Let's see if testing on a bigger dataset helps. |
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@mlpack-jenkins test this |
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Another great contribution, thanks! |
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Thanks. :) I am planning to implement approximate nearest neighbours using the NearPy library for the benchmarks . Will make a PR by tonight. |
Iron-Stark
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Apr 6, 2017
Added more options like min_samples_split,min_samples_leaf,max_features and max_nodes in Decision Tree scikit implementation.
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@rcurtin
I have added this PR in place of PR(#39) after updating my fork. Please Review.