<jupyter_start><jupyter_text>Score Treinamento<jupyter_code>score = regressor.score(X_treinamento,y_treinamento)
score
previsoes = regressor.predict(X_teste)
from sklearn.metrics import mean_absolute_error,mean_squared_error
mae = mean_absolute_error(y_teste,previsoes)
mae
mse = mean_squared_error(y_teste,previsoes)
mse
resultado  = abs(y_teste-previsoes)
print(resultado)
resultado.mean()<jupyter_output><empty_output><jupyter_text>Score Teste<jupyter_code>score = regressor.score(X_teste,y_teste)
score<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.09514925373134328, 'fraction_numerical': 0.0, 'mean_word_length': 13.916666666666666, 'pattern_counts': {'":': 0, '<': 9, '<?xml version=': 0, '>': 9, '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': '76655', 'license': 'MIT', 'n_tokens_mistral': 222, 'n_tokens_neox': 214, 'n_words': 23, 'path': 'notebook/_RegressaoLinear/RegressaoLinearM_PrecoCasas.ipynb', 'repo_name': 'victorblois/py_datascience', 'size': '536'}