<jupyter_start><jupyter_text>Modelo MLP<jupyter_code>model = MLPClassifier(max_iter=2000, random_state=RANDOM_NUM, solver="sgd")
model.fit(X_train, y_train)
predict = model.predict(X_test)
model.get_params()
clf = MLPClassifier(max_iter=2000, random_state=RANDOM_NUM)
model = GridSearchCV(clf, {}, n_jobs=-1, cv=10)
model.fit(X_train, y_train)
predict = model.predict(X_test)
accuracy = round(accuracy_score(y_test, predict), 4)
accuracy
model.get_params()
plot_confusion_matrix(model, X_test, y_test)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1318267419962335, 'fraction_numerical': 0.022598870056497175, 'mean_word_length': 11.090909090909092, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, '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': '448400', 'license': 'MIT', 'n_tokens_mistral': 224, 'n_tokens_neox': 214, 'n_words': 30, 'path': 'experimentos/veiculos.ipynb', 'repo_name': 'pythrick/laboratorio-ia', 'size': '531'}