model.fit(df_x, df_y)
时间: 2024-04-23 20:25:31 浏览: 14
这是一行Python代码,用于对线性回归模型model进行训练。具体来说,它会使用输入变量df_x和输出变量df_y来训练模型,从而拟合输入和输出之间的线性关系。在scikit-learn中,LinearRegression类的fit()方法用于拟合线性回归模型。fit()方法会修改模型的内部参数,使其能够对新的输入数据进行预测。
相关问题
修改代码,使用其他方式跳过错误行,import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB #加载 CSV 文件并忽略错误行 train_df = pd.read_csv('train.csv', encoding='utf-8', error_bad_lines=False) test_df = pd.read_csv('test.csv', encoding='utf-8', error_bad_lines=False) #删除无效行 train_df.dropna(inplace=True) test_df.dropna(inplace=True) #划分训练集和测试集 X_train = train_df['content'] y_train = train_df['category'] X_test = test_df['content'] y_test = test_df #特征提取 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) #训练模型 model = MultinomialNB() model.fit(X_train, y_train) #测试模型 score = model.score(X_test, y_test) print('Accuracy:', score)
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
#加载 CSV 文件并忽略错误行
try:
train_df = pd.read_csv('train.csv', encoding='utf-8')
except:
pass
try:
test_df = pd.read_csv('test.csv', encoding='utf-8')
except:
pass
#删除无效行
train_df.dropna(inplace=True)
test_df.dropna(inplace=True)
#划分训练集和测试集
X_train = train_df['content']
y_train = train_df['category']
X_test = test_df['content']
y_test = test_df
#特征提取
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)
#训练模型
model = MultinomialNB()
model.fit(X_train, y_train)
#测试模型
score = model.score(X_test, y_test)
print('Accuracy:', score)
import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.model_selection import train_test_split from sklearn.naive_bayes import MultinomialNB # 加载 CSV 文件 train_df = pd.read_csv('train.csv', encoding='utf-8', error_bad_lines=False) test_df = pd.read_csv('test.csv', encoding='utf-8', error_bad_lines=False) # 删除无效行 train_df.dropna(inplace=True) test_df.dropna(inplace=True) # 划分训练集和测试集 X_train = train_df['content'] y_train = train_df['category'] X_test = test_df['content'] y_test = test_df['category'] # 特征提取 vectorizer = TfidfVectorizer() X_train = vectorizer.fit_transform(X_train) X_test = vectorizer.transform(X_test) # 训练模型 model = MultinomialNB() model.fit(X_train, y_train) # 测试模型 score = model.score(X_test, y_test) print('Accuracy:', score)修改以上代码,使其使用error_bad_lines=False
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
# 加载 CSV 文件并忽略错误行
train_df = pd.read_csv('train.csv', encoding='utf-8', error_bad_lines=False)
test_df = pd.read_csv('test.csv', encoding='utf-8', error_bad_lines=False)
# 删除无效行
train_df.dropna(inplace=True)
test_df.dropna(inplace=True)
# 划分训练集和测试集
X_train = train_df['content']
y_train = train_df['category']
X_test = test_df['content']
y_test = test_df['category']
# 特征提取
vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)
# 训练模型
model = MultinomialNB()
model.fit(X_train, y_train)
# 测试模型
score = model.score(X_test, y_test)
print('Accuracy:', score)
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