尽管没有一维数组，但在Sklearn中通过一维数组获取弃用警告

尽管没有一维数组，但在Sklearn中通过一维数组获取弃用警告

该错误来自预测方法。Numpy将[1,1]解释为一维数组。因此，这应该避免警告：

clf.predict(np.array([[1,1]]))

注意：

In [14]: p1 = np.array([1,1])In [15]: p1.shapeOut[15]: (2,)In [16]: p2 = np.array([[1,1]])In [17]: p2.shapeOut[17]: (1, 2)

另外，请注意，您不能使用形状（2,1）的数组

In [21]: p3 = np.array([[1],[1]])In [22]: p3.shapeOut[22]: (2, 1)In [23]: clf.predict(p3)---------------------------------------------------------------------------ValueError          Traceback (most recent call last)<ipython-input-23-e4070c037d78> in <module>()----> 1 clf.predict(p3)/home/juan/anaconda3/lib/python3.5/site-packages/sklearn/svm/base.py in predict(self, X)    566  Class labels for samples in X.    567         """--> 568         y = super(baseSVC, self).predict(X)    569         return self.classes_.take(np.asarray(y, dtype=np.intp))    570/home/juan/anaconda3/lib/python3.5/site-packages/sklearn/svm/base.py in predict(self, X)    303         y_pred : array, shape (n_samples,)    304         """--> 305         X = self._validate_for_predict(X)    306         predict = self._sparse_predict if self._sparse else self._dense_predict    307         return predict(X)/home/juan/anaconda3/lib/python3.5/site-packages/sklearn/svm/base.py in _validate_for_predict(self, X)    472  raise ValueError("X.shape[1] = %d should be equal to %d, "    473        "the number of features at training time" %--> 474        (n_features, self.shape_fit_[1]))    475         return X    476ValueError: X.shape[1] = 1 should be equal to 2, the number of features at training time