我有一个项目数据集，我想使用 Python 和 scikit-learn 预测结果(成功/失败)。数据集包含多种数据类型:项目名称、项目描述等都是文本列。另一方面，项目成本是一个数字字段。
我想使用 TF-IDF 转换文本列，我可以将其用作我的模型的输入。这是我的代码:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)
tfidf_transformer = TfidfTransformer()
X_train['Project Title'] = tfidf_transformer.fit_transform(X_train['Project Title'])
但是我得到了错误:
TypeError: no supported conversion for types: (dtype('O'),)
知道为什么会出现这个错误吗？
编辑:下面的示例数据
Project Title Project Essay Project Short Description Project Need Statement Project Cost Project Type ID Project Subject Category Tree ID Project Subject Subcategory Tree ID Project Resource Category ID Project Grade Level Category ID Project Current Status ID
Stand Up to Bullying: Together We Can! Did you know that 1-7 students in grades K-12 ... Did you know that 1-7 students in grades K-12 ... My students need 25 copies of "Bullying in Sch... 361.80 0 0 0 0 0 0
最佳答案
问题是您使用 TfidfTransformer 将计数矩阵转换为规范化 tf 或 tf-idf 表示，而不是使用 TfidfVectorizer 将原始文档集合转换为矩阵TF-IDF 特征
from sklearn.feature_extraction.text import TfidfVectorizer
X = pd.DataFrame({'Project Title': ['hello stackoverflow', 'text column', 'scikit learn', 'machine learning projects']})
vect = TfidfVectorizer(ngram_range=(1, 2))
tfidf = vect.fit_transform(X['Project Title'])
X_tfidf = pd.DataFrame(matrix.todense(), columns=vect.get_feature_names())
关于Python scikit-学习 : prediction on dataset with text and numeric variables，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/52905243/