我使用RandomForest创建了一个模型，该模型具有98％的准确性。我使用pickle保存模型并预测新数据集。我的输入字符串是纯文本，因此无法将其传递给模型。我试图进行矢量化和解析，但是并没有帮助。
import pickle
modelFile=os.path.join('D:\PYPrograms','Data','model')
with open(modelFile, 'rb') as training_model:
model = pickle.load(training_model)
tf2 = CountVectorizer()
File=os.path.join('D:\PYPrograms','Data','POS','TestData.csv')
data = pd.read_csv(File)
data.columns = data.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')
data.loc[:,"title"] = data.title.apply(lambda x : " ".join(re.findall('[\w]+',x)))
df2 = data
df3 = df2["tickettype"]+" "+df2["title"]
#cv_data2 = tf1.transform(df2["type"])
cv_data = tf2.transform(df3)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-205-c0ac8462bce6> in <module>
----> 1 model.predict(test)
~\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\ensemble\forest.py in predict(self, X)
543 The predicted classes.
544 """
--> 545 proba = self.predict_proba(X)
546
547 if self.n_outputs_ == 1:
~\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\ensemble\forest.py in predict_proba(self, X)
586 check_is_fitted(self, 'estimators_')
587 # Check data
--> 588 X = self._validate_X_predict(X)
589
590 # Assign chunk of trees to jobs
~\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\ensemble\forest.py in _validate_X_predict(self, X)
357 "call `fit` before exploiting the model.")
358
--> 359 return self.estimators_[0]._validate_X_predict(X, check_input=True)
360
361 @property
~\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\tree\tree.py in _validate_X_predict(self, X, check_input)
400 "match the input. Model n_features is %s and "
401 "input n_features is %s "
--> 402 % (self.n_features_, n_features))
403
404 return X
ValueError: Number of features of the model must match the input. Model n_features is 6639 and input n_features is 3
数据可从https://drive.google.com/open?id=1xaKKSXzpr7THezqU_8jycfvAueg0nnCQ
获得