Question

使用sklearn SVC（），出现以下错误

import sklearn

from sklearn.datasets import load_iris

iris = load_iris()

X, y = iris.data, iris.target

from sklearn.svm import SVC

# create the model
mySVC = SVC()

# fit the model to data
mySVC.fit(X,y)

# test the model on (new) data
result = mySVC.predict([3, 5, 4, 2])
print(result)
print(iris.target_names[result])

ValueError                                Traceback (most recent call last)
<ipython-input-47-8994407a09e3> in <module>()
      1 # test the model on (new) data
----> 2 result = mySVC.predict([3, 5, 4, 2])
      3 print(result)
      4 print(iris.target_names[result])

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/svm/base.py in predict(self, X)
    546             Class labels for samples in X.
    547         """
--> 548         y = super(BaseSVC, self).predict(X)
    549         return self.classes_.take(np.asarray(y, dtype=np.intp))
    550 

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/svm/base.py in predict(self, X)
    306         y_pred : array, shape (n_samples,)
    307         """
--> 308         X = self._validate_for_predict(X)
    309         predict = self._sparse_predict if self._sparse else self._dense_predict
    310         return predict(X)

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/sklearn/svm/base.py in _validate_for_predict(self, X)
    437         check_is_fitted(self, 'support_')
    438 
--> 439         X = check_array(X, accept_sparse='csr', dtype=np.float64, order="C")
    440         if self._sparse and not sp.isspmatrix(X):
    441             X = sp.csr_matrix(X)

/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/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)
    439                     "Reshape your data either using array.reshape(-1, 1) if "
    440                     "your data has a single feature or array.reshape(1, -1) "
--> 441                     "if it contains a single sample.".format(array))
    442             array = np.atleast_2d(array)
    443             # To ensure that array flags are maintained

ValueError: Expected 2D array, got 1D array instead:
array=[3. 5. 4. 2.].
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.

Answer 1

正如所提到的错误，您将必须传递二维数组。您可以尝试使用以下方法：

result = mySVC.predict([[3, 5, 4, 2]])

您需要传递样本，这里的每个样本都是一个数组，所以您传递的只是一个样本（因为一个样本具有4个特征）而不是样本。请注意，对于按顺序传递给预测的每个样本，您还将收到预测的数组/列表。

来自documentation：

预测（X）


对X中的样本进行分类。

对于一类模型，返回+1或-1。

参数：


X：{类似数组，稀疏矩阵}，形状（n_samples，n_features）       对于kernel =“ precomputed”，X的预期形状为[n_samples_test，       n_samples_train]


返回：


y_pred：数组，形状（n_samples个）

X中样本的类别标签。

预测在python中使用SVC时出现“ ValueError：预期的2D数组，取而代之的是1D数组”

1 个答案: