尝试拟合数据集时遇到错误Unknown label type: (array([0.14999, 0.175 , 0.225 , ..., 4.991 , 5. , 5.00001]),)。
from sklearn.datasets import fetch_california_housing
from sklearn.datasets import load_iris
cali = fetch_california_housing()
iris = load_iris()
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB() # probabilistic
y_pred_cali = gnb.fit(cali.data, cali.target).predict(cali.data)
错误：
ValueError Traceback (most recent call last)
<ipython-input-23-71ed3304ef0f> in <module>
14
15 gnb = GaussianNB() # probabilistic
---> 16 y_pred_cali = gnb.fit(cali[0], cali[1]).predict(cali[0])
17
~\Anaconda3\lib\site-packages\sklearn\naive_bayes.py in fit(self, X, y, sample_weight)
189 X, y = check_X_y(X, y)
190 return self._partial_fit(X, y, np.unique(y), _refit=True,
--> 191 sample_weight=sample_weight)
192
193 @staticmethod
~\Anaconda3\lib\site-packages\sklearn\naive_bayes.py in _partial_fit(self, X, y, classes, _refit, sample_weight)
351 self.classes_ = None
352
--> 353 if _check_partial_fit_first_call(self, classes):
354 # This is the first call to partial_fit:
355 # initialize various cumulative counters
~\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py in _check_partial_fit_first_call(clf, classes)
318 else:
319 # This is the first call to partial_fit
--> 320 clf.classes_ = unique_labels(classes)
321 return True
322
~\Anaconda3\lib\site-packages\sklearn\utils\multiclass.py in unique_labels(*ys)
92 _unique_labels = _FN_UNIQUE_LABELS.get(label_type, None)
93 if not _unique_labels:
---> 94 raise ValueError("Unknown label type: %s" % repr(ys))
95
96 ys_labels = set(chain.from_iterable(_unique_labels(y) for y in ys))
ValueError: Unknown label type: (array([0.14999, 0.175 , 0.225 , ..., 4.991 , 5. , 5.00001]),)
答案 0 :(得分：0)
此数据集具有连续目标变量。
GNB是一种分类方法，而不是回归方法。 Y必须是离散类，而不是连续变量。