"ValueError: Expected 2D array, got 1D array instead" while predicting using SVC in python

Question

Using sklearn SVC(), I am getting the below error

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.

niraj · Accepted Answer · 2018-09-16 01:47:05Z

2

As error mentioned, you will have to pass 2-D array. You can try using as following:

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

You need to pass samples, here each sample is an array, so what are you passing is just one sample (as one sample has 4 features) not samples. Note that you will receive array/list of predictions as well for each samples passed for prediction in order.

From documentation:

predict(X)

Perform classification on samples in X.

For an one-class model, +1 or -1 is returned.

Parameters:

X : {array-like, sparse matrix}, shape (n_samples, n_features) For kernel=”precomputed”, the expected shape of X is [n_samples_test, n_samples_train]

Returns:

y_pred : array, shape (n_samples,)

Class labels for samples in X.

edited Sep 16, 2018 at 1:47

answered Sep 16, 2018 at 1:41

niraj

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