选择模型后，当我尝试使用它时，我收到错误 -
“NotFittedError：尚未安装此 MultiLabelBinarizer 实例。在使用此估算器之前，请使用适当的参数调用 'fit'。”
X = <training_data>
y = <training_labels>
# Perform multi-label classification on class labels.
mlb = MultiLabelBinarizer()
multilabel_y = mlb.fit_transform(y)
p = Pipeline([
('vect', CountVectorizer(min_df=min_df, ngram_range=ngram_range)),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(clf))
])
# Use multilabel classes to fit the pipeline.
p.fit(X, multilabel_y)
此代码将起作用。让我们sklearn.linear_model.LogisticRegression为您处理多分类。
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
X = ["How to join amazon company ","How to join google ",'Stay home']
y = ["Career Advice", "Fresher",'Other' ]
# Perform multi-label classification on class labels.
clf = LogisticRegression()
p = Pipeline([
('vect', CountVectorizer()),
('tfidf', TfidfTransformer()),
('clf', OneVsRestClassifier(clf))
])
# Use multilabel classes to fit the pipeline.
p.fit(X, y);
p.predict(X)
```