我看过其他帖子谈论这个，但其中任何人都可以帮助我。我在 Windows x6 机器上使用带有 Python 3.6.0 的 jupyter notebook。我有一个大数据集，但我只保留其中一部分来运行我的模型：
这是我使用的一段代码：
df = loan_2.reindex(columns= ['term_clean','grade_clean', 'annual_inc', 'loan_amnt', 'int_rate','purpose_clean','installment','loan_status_clean'])
df.fillna(method= 'ffill').astype(int)
from sklearn.preprocessing import Imputer
from sklearn.preprocessing import StandardScaler
imp = Imputer(missing_values='NaN', strategy='median', axis=0)
array = df.values
y = df['loan_status_clean'].values
imp.fit(array)
array_imp = imp.transform(array)
y2= y.reshape(1,-1)
imp.fit(y2)
y_imp= imp.transform(y2)
X = array_imp[:,0:4]
Y = array_imp[:,4]
validation_size = 0.20
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
seed = 7
scoring = 'accuracy'
from sklearn import model_selection
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import BernoulliNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.ensemble import AdaBoostClassifier
from sklearn.neural_network import MLPClassifier
# Spot Check Algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('BNB', BernoulliNB()))
models.append(('RF', RandomForestClassifier()))
models.append(('GBM', AdaBoostClassifier()))
models.append(('NN', MLPClassifier()))
models.append(('SVM', SVC()))
# evaluate each model in turn
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
results.append(cv_results)
names.append(name)
msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
print(msg)
当我运行最后一段代码时，出现此错误：
ValueError Traceback (most recent call last)
<ipython-input-262-1e6860ba615b> in <module>()
4 for name, model in models:
5 kfold = model_selection.KFold(n_splits=10, random_state=seed)
----> 6 cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
7 results.append(cv_results)
8 names.append(name)
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py in cross_val_score(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch)
138 train, test, verbose, None,
139 fit_params)
--> 140 for train, test in cv_iter)
141 return np.array(scores)[:, 0]
142
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self, iterable)
756 # was dispatched. In particular this covers the edge
757 # case of Parallel used with an exhausted iterator.
--> 758 while self.dispatch_one_batch(iterator):
759 self._iterating = True
760 else:
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in dispatch_one_batch(self, iterator)
606 return False
607 else:
--> 608 self._dispatch(tasks)
609 return True
610
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in _dispatch(self, batch)
569 dispatch_timestamp = time.time()
570 cb = BatchCompletionCallBack(dispatch_timestamp, len(batch), self)
--> 571 job = self._backend.apply_async(batch, callback=cb)
572 self._jobs.append(job)
573
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in apply_async(self, func, callback)
107 def apply_async(self, func, callback=None):
108 """Schedule a func to be run"""
--> 109 result = ImmediateResult(func)
110 if callback:
111 callback(result)
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py in __init__(self, batch)
324 # Don't delay the application, to avoid keeping the input
325 # arguments in memory
--> 326 self.results = batch()
327
328 def get(self):
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in __call__(self)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\externals\joblib\parallel.py in <listcomp>(.0)
129
130 def __call__(self):
--> 131 return [func(*args, **kwargs) for func, args, kwargs in self.items]
132
133 def __len__(self):
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\model_selection\_validation.py in _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, error_score)
236 estimator.fit(X_train, **fit_params)
237 else:
--> 238 estimator.fit(X_train, y_train, **fit_params)
239
240 except Exception as e:
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\linear_model\logistic.py in fit(self, X, y, sample_weight)
1172 X, y = check_X_y(X, y, accept_sparse='csr', dtype=np.float64,
1173 order="C")
-> 1174 check_classification_targets(y)
1175 self.classes_ = np.unique(y)
1176 n_samples, n_features = X.shape
C:\Users\dalila\Anaconda\lib\site-packages\sklearn\utils\multiclass.py in check_classification_targets(y)
170 if y_type not in ['binary', 'multiclass', 'multiclass-multioutput',
171 'multilabel-indicator', 'multilabel-sequences']:
--> 172 raise ValueError("Unknown label type: %r" % y_type)
173
174
ValueError: Unknown label type: 'continuous'
简要假设：我的数据通常没有 NaN 和缺失值。