<jupyter_start><jupyter_text>Perceptron<jupyter_code>p = Perceptron(
             # random_state=42,
              max_iter=10,
              tol=0.001,
              #verbose = True
              )
p.fit(X_train, y_train)
print("Cantidad de iteraciones: " +str(p.n_iter_))
disp = metrics.plot_confusion_matrix(p, X_test, y_test,cmap=plt.cm.Blues)
disp.ax_.set_title('Confusion Matrix')
plt.show()<jupyter_output>Cantidad de iteraciones: 9<jupyter_text>Perceptron multicapa<jupyter_code>#activation = "identity"
activation = "logistic"
#activation = "tanh"
#activation = "relu"
mlp = MLPClassifier(#random_state=42,
                    hidden_layer_sizes=(30,30),
                    max_iter = 1000,
                    activation = activation,
                    verbose = True
                    )
mlp.fit(X_train, y_train)

print("Cantidad de iteraciones: " +str(mlp.n_iter_))
disp = metrics.plot_confusion_matrix(mlp, X_test, y_test,cmap=plt.cm.Blues)
disp.ax_.set_title('Confusion Matrix')
plt.show()<jupyter_output>Cantidad de iteraciones: 679
{'fraction_non_alphanumeric': 0.11787072243346007, 'fraction_numerical': 0.02091254752851711, 'mean_word_length': 3.05, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '538689', 'license': 'Apache-2.0', 'n_tokens_mistral': 406, 'n_tokens_neox': 371, 'n_words': 56, 'path': 'tp8/Iris.ipynb', 'repo_name': 'xRUIZxMATIASx/inteligencia-artificial', 'size': '1052'}