我有一个包含稀疏矩阵的列表：
<1x248 sparse matrix of type '<class 'numpy.int64'>'
with 5 stored elements in Compressed Sparse Row format>
当我尝试使用 sckit learn fit 时出现错误：
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train,y_train)
> ValueError Traceback (most recent call last)
<ipython-input-103-79c5ac7b2aa7> in <module>()
1 from sklearn.naive_bayes import GaussianNB
2 classifier = GaussianNB()
----> 3 classifier.fit(X_train,y_train)
3 frames
/usr/local/lib/python3.7/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
554 "Reshape your data either using array.reshape(-1, 1) if "
555 "your data has a single feature or array.reshape(1, -1) "
--> 556 "if it contains a single sample.".format(array))
557
558 # in the future np.flexible dtypes will be handled like object dtypes
ValueError: Expected 2D array, got 1D array instead:
array=[<1x248 sparse matrix of type '<class 'numpy.int64'>'
with 5 stored elements in Compressed Sparse Row format>
<1x248 sparse matrix of type '<class 'numpy.int64'>'
with 4 stored elements in Compressed Sparse Row format>
<1x248 sparse matrix of type '<class 'numpy.int64'>'
with 2 stored elements in Compressed Sparse Row format>
...
<1x248 sparse matrix of type '<class 'numpy.int64'>'
with 4 stored elements in Compressed Sparse Row format>
<1x248 sparse matrix of type '<class 'numpy.int64'>'
with 1 stored elements in Compressed Sparse Row format>
<1x248 sparse matrix of type '<class 'numpy.int64'>'
with 1 stored elements in Compressed Sparse Row format>].
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.