A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
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[BUG] Instead of underperforming it is written outperforming in 07_ensemble_learning_and_random_forests.ipynb #151
Describe the bug
Ok so this typo I found while reading 07_ensemble_learning_and_random_forests.ipynb
In the markdown cell after cell number Out[44] we can see it is written: "The linear SVM is far outperformed by the other classifiers. However, let's keep it for now since it may improve the voting classifier's performance."
But as we can see the score for LinearSVC is the least i.e. 0.8662.
Expected behavior
Instead we can write: "The Linear SVM is actually the worst-performing model among the four, with a score of 0.8662, which is lower than the other three models. Instead Extra Trees is the best-performing model among the four, with a score of 0.9743."
Screenshots
Here we can see it is written Linear SVM is far outperformed instead it should be underperformed.
Versions (please complete the following information):
Describe the bug Ok so this typo I found while reading 07_ensemble_learning_and_random_forests.ipynb In the markdown cell after cell number Out[44] we can see it is written: "The linear SVM is far outperformed by the other classifiers. However, let's keep it for now since it may improve the voting classifier's performance."
But as we can see the score for LinearSVC is the least i.e. 0.8662.
Expected behavior Instead we can write: "The Linear SVM is actually the worst-performing model among the four, with a score of 0.8662, which is lower than the other three models. Instead Extra Trees is the best-performing model among the four, with a score of 0.9743."
Screenshots Here we can see it is written Linear SVM is far outperformed instead it should be underperformed.
Versions (please complete the following information):