Add MLP activation function comparison script #12915
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Added a new script (mlp_activation_comparison.py) that demonstrates the effect of different activation functions
('relu', 'tanh', 'logistic') on a simple dataset using scikit-learn's MLPClassifier.
This helps visualize and understand how activation choices influence model performance.
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| from sklearn.metrics import accuracy_score | ||
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| X, y = make_moons(n_samples=500, noise=0.2, random_state=42) | ||
| X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42) |
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Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_train
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_test
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Repository:
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Automated review generated by algorithms-keeper. If there's any problem regarding this review, please open an issue about it.
algorithms-keeper commands and options
algorithms-keeper actions can be triggered by commenting on this PR:
@algorithms-keeper reviewto trigger the checks for only added pull request files@algorithms-keeper review-allto trigger the checks for all the pull request files, including the modified files. As we cannot post review comments on lines not part of the diff, this command will post all the messages in one comment.NOTE: Commands are in beta and so this feature is restricted only to a member or owner of the organization.
| from sklearn.metrics import accuracy_score | ||
|
|
||
| X, y = make_moons(n_samples=500, noise=0.2, random_state=42) | ||
| X_train, X_test, y_train, y_test = train_test_split( |
There was a problem hiding this comment.
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_train
Variable and function names should follow the snake_case naming convention. Please update the following name accordingly: X_test
Added a new script:
mlp_activation_comparison.pyThis script demonstrates the effect of different activation functions (
relu,tanh,logistic) on a simple dataset using scikit-learn's MLPClassifier.It helps visualize and understand how activation choices influence model performance.
Describe your change:
Checklist:
Fixes #ISSUE-NUMBER).