Update decision tree ensemble notebook to not use exec #1
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I appreciated the youtube series and wanted to give back with my expertise! This code doesn't need exec, and exec and eval are considered anti-patterns, with some security implications. https://stackoverflow.com/questions/1933451/why-should-exec-and-eval-be-avoided
teastburn
commented
Dec 23, 2023
| "AdaBoostClassifier 0.956 0.970 0.963 \n", | ||
| "GradientBoostingClassifier 0.970 0.970 0.970 " | ||
| ] | ||
| "text/plain": " precision_train recall_train f1_train \\\nDecisionTreeClassifier 1.0 1.0 1.0 \nRandomForestClassifier 1.0 1.0 1.0 \nAdaBoostClassifier 1.0 1.0 1.0 \nGradientBoostingClassifier 1.0 1.0 1.0 \n\n precision_test recall_test f1_test \nDecisionTreeClassifier 0.952 0.896 0.923 \nRandomForestClassifier 0.985 0.955 0.970 \nAdaBoostClassifier 0.956 0.970 0.963 \nGradientBoostingClassifier 0.970 0.970 0.970 ", |
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i'm not sure why my pycharm/jupyter setup reformatted this. the previous formatting was more readable. but i wanted to be sure the correctness of the output was the same, so i included it here.
teastburn
commented
Dec 23, 2023
|
Thanks for refactoring this @teastburn! Since the video and blog review the original example code, I want to preserve it. However, your version is also helpful. Would you mind making another request where your version is a separate file named "tree_ensemble_example-no_exec.ipynb"? |
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Why
I appreciated the youtube series and wanted to give back with my expertise!
What
This code doesn't need exec, and exec and eval are considered anti-patterns, with some security implications. Particularly in a shared repo where the assumption is "trust me to run this code".
https://stackoverflow.com/questions/1933451/why-should-exec-and-eval-be-avoided
Tested
I tested it by running it in a Jupyter notebook in Pycharm and verifying the outputs by my eye. Certainly not the most foolproof testing method.