DOC Fix typos, wording in plot_gradient_boosting_regression.py#16894
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thomasjpfan merged 2 commits intoscikit-learn:masterfrom Apr 11, 2020
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DOC Fix typos, wording in plot_gradient_boosting_regression.py#16894thomasjpfan merged 2 commits intoscikit-learn:masterfrom
thomasjpfan merged 2 commits intoscikit-learn:masterfrom
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thomasjpfan
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Apr 10, 2020
| # However there are many other options (check | ||
| # :class:`~sklearn.ensemble.GradientBoostingRegressor` to see what are | ||
| # other possibilities) | ||
| # loss : loss function to optimize. Least squares function used in this case |
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| # loss : loss function to optimize. Least squares function used in this case | |
| # loss : loss function to optimize. The least squares function is used in this case |
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| clf = ensemble.GradientBoostingRegressor(**params) | ||
| clf.fit(X_train, y_train) | ||
| reg = ensemble.GradientBoostingRegressor(**params) |
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This change is much appreciated.
| # predictive feature, "bp", is also the same for the 2 methods. The remaining | ||
| # For this example, the impurity-based and permutation methods identify the | ||
| # same 2 strongly predictive features but not in the same order. The third most | ||
| # predictive feature, ``bp``, is also the same for the 2 methods. The remaining |
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I am +0 on this change, since "bp" is not code.
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| # predictive feature, ``bp``, is also the same for the 2 methods. The remaining | |
| # predictive feature, "bp", is also the same for the 2 methods. The remaining |
| # regression model. You can play with those parameters to see how the | ||
| # results change: | ||
| # for testing. We will also set the regression model parameters. You can play | ||
| # with these parameters to see how the results change: |
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| # with these parameters to see how the results change: | |
| # with these parameters to see how the results change. |
NicolasHug
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Thanks @lucyleeow , LGTM when comments are addressed
| # | ||
| # Finally, we will visualize the results. To do that we will first compute the | ||
| # test set deviance and then plot it. | ||
| # test set deviance and then plot it against boosting iteration. |
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| # test set deviance and then plot it against boosting iteration. | |
| # test set deviance and then plot it against boosting iterations. |
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Thanks @thomasjpfan and @NicolasHug. Suggestions added. |
thomasjpfan
approved these changes
Apr 11, 2020
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What does this implement/fix? Explain your changes.
We set random state to be consistent with the result.as code below it does not userandom_statelossparameter does, which is done for all previous parametersclftoregin the code as this is regression modelAny other comments?