ValueError：不明なラベルの種類： 'continuous'

Question

これについて他の投稿が話しているのを見たことがありますが、これらのどれでも私を助けることができます。私はWindows x6マシンでPython 3.6.0でjupyterノートブックを使用しています。 私は大規模なデータセットを持っていますが、私は私のモデルを実行することの唯一の作品を保つ：

これは私が使用したコードの一部です：

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) 

を、私は、コードの最後の一枚にこれを実行すると、

---

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' 

簡単な仮定：私のデータがNaNと一般的に欠損値からクリーンでエラーがアップします。この2行の istead：

from sklearn.svm import SVC 
.. 
.. 
models.append(('SVM', SVC())) 

使用これら：

Answer 1

あなたの問題の解決策は、あなたが代わりにその分類モデルの回帰モデルを必要とするということである

from sklearn.svm import SVR 
.. 
.. 
models.append(('SVM', SVR())) 

Answer 2

分類子Y_trainに期待するのみ整数値（クラスのラベル）。しかし、浮動小数点になり、このエラーが発生します。 回帰を行う場合は、分類子の代わりに回帰子を使用します。 分類が必要な場合は、y_trainをチェックしてください。あなたのコードのこの一部とすることができる、それが浮かぶように変換：

imp = Imputer(missing_values='NaN', strategy='median', axis=0) 
array = df.values 
imp.fit(array) 
array_imp = imp.transform(array) 
Y = array_imp[:,4] 

あなたの `Y_train`を印刷する必要があります

Y = array[:,4] # take it from not changed data 
imp = Imputer(missing_values='NaN', strategy='median', axis=0) 
array = df.values 
imp.fit(array) 
array_imp = imp.transform(array)