caret::train support for greedyMSE #305
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WalkthroughThe recent updates enhance the Changes
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Files selected for processing (4)
- NAMESPACE (1 hunks)
- R/greedyOpt.R (4 hunks)
- man/predict.greedyMSE.Rd (1 hunks)
- tests/testthat/test-greedyMSE.R (4 hunks)
Additional comments not posted (16)
man/predict.greedyMSE.Rd (1)
7-7: Documentation update approved.The addition of the
return_labelsparameter is well-documented and aligns with the functionality changes. Ensure that this documentation is consistent with the actual implementation.Also applies to: 14-15
NAMESPACE (1)
25-25: Namespace updates approved.The addition of the
varImpmethod forgreedyMSEand the export ofgreedyMSE_caretenhance the package's integration and usability.Also applies to: 33-33
R/greedyOpt.R (5)
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13-72:
FunctiongreedyMSEupdates approved.The changes improve data handling and ensure consistency in processing. The enhancements for handling
Yas a factor are particularly useful for classification tasks.
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86-93:
Functionprint.greedyMSEapproved.The function correctly prints the RMSE and model weights for a
greedyMSEobject.
102-108: FunctionvarImp.greedyMSEapproved.The calculation of variable importance is straightforward and effectively implemented.
118-139: Functionpredict.greedyMSEupdates approved.The addition of the
return_labelsparameter enhances flexibility for classification tasks. The function handles predictions effectively.
159-185: FunctiongreedyMSE_caretapproved.The function is well-structured and effectively integrates
greedyMSEwith thecaretpackage, enhancing usability.tests/testthat/test-greedyMSE.R (9)
4-4: LGTM! Column naming enhances clarity.The addition of column names to
XandYimproves dataset usability.Also applies to: 16-16
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39-49:
Binary classification test looks good.The test case effectively checks the accuracy and output of the
greedyMSEmodel for binary classification.
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51-61:
Multiclass classification test is comprehensive.The test case appropriately checks the model's performance for multiclass classification, ensuring accuracy is above random guessing.
201-209: Variable importance test is well-structured.The test case effectively verifies the calculation and normalization of variable importance scores.
211-231: Prediction functionality tests are complete.The tests verify correct handling of data frame inputs and label returns, ensuring predictions are as expected.
233-263: Robustness test with correlated predictors is effective.The test case appropriately validates the model's performance with correlated predictors, ensuring RMSE is lower than the baseline.
265-303: Regression ensembling test is thorough.The test case effectively checks the performance of regression ensembling, ensuring it surpasses individual models.
305-351: Binary classification ensembling test is well-designed.The test case appropriately validates the ensemble model's performance, ensuring it surpasses individual models in AUC.
353-410: Multiclass classification ensembling test is comprehensive.The test case effectively validates the ensemble model's performance, ensuring it surpasses individual models in AUC.
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