<jupyter_start><jupyter_text>Ajustando hiper-parametros:    - iterations=10    SCORE: 0.753151    - iterations=50    SCORE: 0.750000    - iterations=100    SCORE: 0.752101    - iterations=200    SCORE: 0.761555    - iterations=300    SCORE: 0.763130    - iterations=300, learning_rate=0.1    SCORE: 0.763130    - iterations=500    SCORE: 0.765231    - iterations=500, learning_rate=0.05    SCORE: 0.755777    - iterations=10, learning_rate=0.03    SCORE: 0.758929<jupyter_code>model_catb = CatBoostClassifier(iterations=500, depth=6, learning_rate=0.005)
model_catb.fit(X_train, y_train, verbose=False)
y_test_hat = model_catb.predict(X_test)
print("Accuracy score: %f" % (accuracy_score(y_test, y_test_hat)))
model_catb = CatBoostClassifier(iterations=200)
model_catb.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=True)
model_catb.score(X_test, y_test)
model_catb = CatBoostClassifier(iterations=500)
model_catb.fit(X_train, y_train, eval_set=[(X_test, y_test)], verbose=True)
model_catb.score(X_test, y_test)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.12345679012345678, 'fraction_numerical': 0.10351377018043685, 'mean_word_length': 5.5875, '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': '641522', 'license': 'MIT', 'n_tokens_mistral': 497, 'n_tokens_neox': 413, 'n_words': 63, 'path': 'TP2/Matias/11_CatBoost.ipynb', 'repo_name': 'aleacevedo/Datos-TP1', 'size': '1053'}