高斯朴素贝叶斯分类的数据类型--使用sklearn如何清理数据

Question

我试图根据手机的特性对其进行分类，但当我通过sklearn应用高斯NB代码时，由于以下错误，我无法这样做:代码：

clf = GaussianNB()
clf.fit(X_train,y_train)
GaussianNB()
accuracy = clf.score(X_test,y_test)
print(accuracy)

错误：

ValueError                                Traceback (most recent call last)
<ipython-input-18-e9515ccc2439> in <module>()
      2 clf.fit(X_train,y_train)
      3 GaussianNB()
----> 4 accuracy = clf.score(X_test,y_test)
      5 print(accuracy)

/Users/kiran/anaconda/lib/python3.6/site-packages/sklearn/base.py in score(self, X, y, sample_weight)
    347         """
    348         from .metrics import accuracy_score
--> 349         return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
    350 
    351 

/Users/kiran/anaconda/lib/python3.6/site-packages/sklearn/naive_bayes.py in predict(self, X)
     63             Predicted target values for X
     64         """
---> 65         jll = self._joint_log_likelihood(X)
     66         return self.classes_[np.argmax(jll, axis=1)]
     67 

/Users/kiran/anaconda/lib/python3.6/site-packages/sklearn/naive_bayes.py in _joint_log_likelihood(self, X)
    422         check_is_fitted(self, "classes_")
    423 
--> 424         X = check_array(X)
    425         joint_log_likelihood = []
    426         for i in range(np.size(self.classes_)):

/Users/kiran/anaconda/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    380                                       force_all_finite)
    381     else:
--> 382         array = np.array(array, dtype=dtype, order=order, copy=copy)
    383 
    384         if ensure_2d:

ValueError: could not convert string to float: 

我的数据集已经被刮过，所以它包含字符串和浮点值。如果有人能建议我如何清理数据和避免错误，这将是有帮助的。

Answer 1

尝试以下几点：

accuracy = clf.score(X_test.astype('float'),y_test.astype('float'))

Answer 2

ValueError: could not convert string to float 

我想这说明了一切。您需要在数据集中将float作为一致的数据类型。

将python中的string转换为float

>>> a = "123.345"
>>> float(a)
>>> 123.345
>>> int(float(a))
>>> 123