Remove the R plotting code for scatter plot.
Remove the R plotting code for SHAP summary plot and partial dependency plot (PDP).
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Add GBDT.
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Used another dataset for demonstration.
- Add Chinese version.
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Add figures of results.
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Change the stop of installing environment.
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Due to the initial parameters are randomly set, some warnings may occur when implement CatBoost. Just ignore it or repeat.
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The categorical features supporting must be rewriten.
- Output the accuracy, best parameters, optimization, and shap results.
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Remove Autogluon.
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Modify NN module.
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Tested on linux (Ubuntu 22.04, WSL2)
- Added lightgbm
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Found that the KernelExplainer can not calculate interaction values.
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Finished the tree based model, and finished the test.
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Plan to extract some same codes from each seperated .py file, and rebuild the whole
models
module. -
The optuna should accomadate the consistent parameters and pass it to the final best parameters set, but now they were all ignored.
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