这很好
cv_results = model_selection.cross_val_score（模型，X_train，Y_train，cv = kfold，得分=得分）
import pandas
from pandas.plotting import scatter_matrix
import matplotlib.pyplot as plt
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 GaussianNB
from sklearn.svm import SVC
# Load dataset (contains floats and one boolean)
url = "\\File\\Path.csv"
names = ['Headers', 'Here', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'T/F']
dataset = pandas.read_csv(url, names=names)
# Split-out validation dataset
array = dataset.values
X = array[:,0:12]
Y = array[:,12]
validation_size = 0.10
seed = 7
X_train, X_validation, Y_train, Y_validation = model_selection.train_test_split(X, Y, test_size=validation_size, random_state=seed)
# Test options and evaluation metric
seed = 7
scoring = 'accuracy'
# Spot check algorithms
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
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)
# Compare Algorithms
fig = plt.figure()
fig.suptitle('Algorithm Comparison')
ax = fig.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()
到达此部分时停止
cv_results = model_selection.cross_val_score（模型，X_train，Y_train，cv = kfold，得分=得分）
Warning (from warnings module):
File "C:\Python\Python37-32\lib\site-packages\sklearn\linear_model\logistic.py", line 433
FutureWarning)
FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.
Warning (from warnings module):
File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 542
FutureWarning)
FutureWarning: From version 0.22, errors during fit will result in a cross validation score of NaN by default. Use error_score='raise' if you want an exception raised or error_score=np.nan to adopt the behavior from version 0.22.
Traceback (most recent call last):
File "/test.py", line 46, in <module>
cv_results = model_selection.cross_val_score(model, X_train, Y_train, cv=kfold, scoring=scoring)
File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 402, in cross_val_score
error_score=error_score)
File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 240, in cross_validate
for train, test in cv.split(X, y, groups))
File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 917, in __call__
if self.dispatch_one_batch(iterator):
File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 759, in dispatch_one_batch
self._dispatch(tasks)
File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 716, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 182, in apply_async
result = ImmediateResult(func)
File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\_parallel_backends.py", line 549, in __init__
self.results = batch()
File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in __call__
for func, args, kwargs in self.items]
File "C:\Python\Python37-32\lib\site-packages\sklearn\externals\joblib\parallel.py", line 225, in <listcomp>
for func, args, kwargs in self.items]
File "C:\Python\Python37-32\lib\site-packages\sklearn\model_selection\_validation.py", line 528, in _fit_and_score
estimator.fit(X_train, y_train, **fit_params)
File "C:\Python\Python37-32\lib\site-packages\sklearn\linear_model\logistic.py", line 1289, in fit
check_classification_targets(y)
File "C:\Python\Python37-32\lib\site-packages\sklearn\utils\multiclass.py", line 171, in check_classification_targets
raise ValueError("Unknown label type: %r" % y_type)
ValueError: Unknown label type: 'unknown'
它运行良好，直到达到
cv_results = model_selection.cross_val_score（模型，X_train，Y_train，cv = kfold，得分=得分）
最佳答案
在制作y_train和y_validator变量后添加以下内容：
Y_train = Y_train.astype('float')
Y_validator = Y_validation.astype('float')
当您读取y变量时，它被存储为一个对象，因此sklearn不知道如何处理它（因此错误ValueError（“ Unknown label type：％r”％y_type）。将Y_train和Y_test更改为浮点数或整数类型应可修复错误