ValueError：标签数= 19与样本数= 1不匹配

这是我使用的代码 . 我正在尝试使用randomforestclassifier根据学习者和主导主题对活动进行分类 .

import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn import linear_model
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from sklearn.metrics import accuracy_score
from sklearn.cross_validation import train_test_split
from sklearn.datasets import make_multilabel_classification
Data = pd.read_excel("F:\VIT material\Master thesis\DATASET.xlsx",names=['learner','Dominant_Subject','Activity'])
print(Data)
print(Data.columns)
Data.reshape(Data.columns.values)
print(Data)
number= LabelEncoder()
Data['learner']= number.fit_transform(Data['learner'].astype('str'))
Data['Dominant_Subject']=number.fit_transform(Data['Dominant_Subject'].astpye('str'))                                      
Data['Activity']= number.fit_transform(Data['Activity'].astype('str'))
print(Data)
print(Data.shape)
X = Data['learner']
print(X)
print(X.shape)
Y = Data['Dominant_Subject'] 
print(Y)
print(Y.shape)
print(len(X))
print(len(Y))
X_train = X[:-5]
X_test = X[-5:]
Y_train = Y[:-10]
Y_test = Y[-10:]
X_train, X_test, Y_train, Y_test=train_test_split(X,test_size=0.2,random_state=20)
print(X_train,X_test,Y_train,Y_test)
model = linear_model.LinearRegression() 
model.fit(X_train,Y_train)
print(model.fit())
clf = RandomForestClassifier(n_estimators=100, min_samples_split=2)
clf.fit(X_train,Y_train)
print(clf.fit())
predicted = clf.predict(X)
print(accuracy_score(predicted,Y))

样本和标签的数量相等但是我仍然得到标签数量不等于样本数量的错误 .

错误追踪：文件“C：/ Users / RAJIV MISHRA / PycharmProjects / mltutorialpractice / 13.py”，第38行，clf.fit（X_train，Y_train）

文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ ensemble \ forest.py”，第326行，适合

for i, t in enumerate(trees))

文件"C:\Users\RAJIV MISHRA\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py"，第758行，在 call 中，而self.dispatch_one_batch（迭代器）：

在dispatch_one_batch中输入文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ externals \ joblib \ parallel.py”，第608行

self._dispatch(tasks)

文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ externals \ joblib \ parallel.py”，第571行，在_dispatch中

job = self._backend.apply_async(batch, callback=cb)

apply_async中的文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ externals \ joblib_parallel_backends.py”，第109行

result = ImmediateResult(func)

文件"C:\Users\RAJIV MISHRA\Anaconda3\lib\site-packages\sklearn\externals\joblib_parallel_backends.py"，第326行，在 init

self.results = batch()

文件"C:\Users\RAJIV MISHRA\Anaconda3\lib\site-packages\sklearn\externals\joblib\parallel.py"，第131行，在 call

return [func(*args, **kwargs) for func, args, kwargs in self.items]

文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ externals \ joblib \ parallel.py”，第131行，

return [func(*args, **kwargs) for func, args, kwargs in self.items]

文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ ensemble \ forest.py”，第120行，_parallel_build_trees

tree.fit(X, y, sample_weight=curr_sample_weight, check_input=False)

文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ tree \ tree.py”，第739行，in fit

X_idx_sorted=X_idx_sorted)

文件“C：\ Users \ RAJIV MISHRA \ Anaconda3 \ lib \ site-packages \ sklearn \ tree \ tree.py”，第240行，适合

"number of samples=%d" % (len(y), n_samples))

ValueError：标签数= 19与样本数= 1不匹配