<jupyter_start><jupyter_text>Multilayer Perceptron: HyperparametersImport [`MLPClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.htmlsklearn.neural_network.MLPClassifier) and [`MLPRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.htmlsklearn.neural_network.MLPRegressor) from `sklearn` and explore the hyperparameters.  Import Multilayer Perceptron Algorithm for Classification & Regression<jupyter_code>from sklearn.neural_network import MLPRegressor, MLPClassifier

print(MLPRegressor())
print(MLPClassifier())<jupyter_output>MLPRegressor()
MLPClassifier()
{'fraction_non_alphanumeric': 0.10213414634146341, 'fraction_numerical': 0.0, 'mean_word_length': 10.526315789473685, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 2, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '249767', 'license': 'MIT', 'n_tokens_mistral': 225, 'n_tokens_neox': 213, 'n_words': 26, 'path': 'ML - Applied Machine Learning - Algorithms/04.Multi-layer Perceptron/01.Multilayer Perceptron - Hyperparameters.ipynb', 'repo_name': 'ptyadana/probability-and-statistics-for-business-and-data-science', 'size': '656'}