使用 sklearn.linear_model.RidgeCV 时出现 MemoryError

我正在尝试训练我的模型

from sklearn.linear_model import RidgeCV
alphas = [0.00001, 0.0001, 0.001, 0.01, 0.01, 0.5, 1, 3,  5]
clf = RidgeCV(alphas=alphas, normalize=True, gcv_mode = 'eigen').fit(x_train, y_train)

其中：

print(x_train.shape, y_train.shape)
(62313, 100600) (62313,)

type(x_train)
scipy.sparse.csr.csr_matrix

在发布之前，我有大约 10 GB 的可用 RAM。启动后，它需要大约 2 GB 并保持空闲 8 字节。我的代码运行了大约半小时，而空闲内存一直在 8 GB 左右，然后崩溃并出现错误：

---------------------------------------------------------------------------
MemoryError                               Traceback (most recent call last)
<ipython-input-9-e9d3d7210319> in <module>()
      2 from sklearn.linear_model import RidgeCV
      3 alphas = [0.00001, 0.0001, 0.001, 0.01, 0.01, 0.5, 1, 3,  5]
----> 4 clf = RidgeCV(alphas=alphas, normalize=True, gcv_mode = 'eigen').fit(x_train, y_train)

~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/ridge.py in fit(self, X, y, sample_weight)
   1112                                   gcv_mode=self.gcv_mode,
   1113                                   store_cv_values=self.store_cv_values)
-> 1114             estimator.fit(X, y, sample_weight=sample_weight)
   1115             self.alpha_ = estimator.alpha_
   1116             if self.store_cv_values:

~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/ridge.py in fit(self, X, y, sample_weight)
   1027         centered_kernel = not sparse.issparse(X) and self.fit_intercept
   1028 
-> 1029         v, Q, QT_y = _pre_compute(X, y, centered_kernel)
   1030         n_y = 1 if len(y.shape) == 1 else y.shape[1]
   1031         cv_values = np.zeros((n_samples * n_y, len(self.alphas)))

~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/ridge.py in _pre_compute(self, X, y, centered_kernel)
    883     def _pre_compute(self, X, y, centered_kernel=True):
    884         # even if X is very sparse, K is usually very dense
--> 885         K = safe_sparse_dot(X, X.T, dense_output=True)
    886         # the following emulates an additional constant regressor
    887         # corresponding to fit_intercept=True

~/anaconda3/lib/python3.6/site-packages/sklearn/utils/extmath.py in safe_sparse_dot(a, b, dense_output)
    133     """
    134     if issparse(a) or issparse(b):
--> 135         ret = a * b
    136         if dense_output and hasattr(ret, "toarray"):
    137             ret = ret.toarray()

~/anaconda3/lib/python3.6/site-packages/scipy/sparse/base.py in __mul__(self, other)
    477             if self.shape[1] != other.shape[0]:
    478                 raise ValueError('dimension mismatch')
--> 479             return self._mul_sparse_matrix(other)
    480 
    481         # If it's a list or whatever, treat it like a matrix

~/anaconda3/lib/python3.6/site-packages/scipy/sparse/compressed.py in _mul_sparse_matrix(self, other)
    500                                     maxval=nnz)
    501         indptr = np.asarray(indptr, dtype=idx_dtype)
--> 502         indices = np.empty(nnz, dtype=idx_dtype)
    503         data = np.empty(nnz, dtype=upcast(self.dtype, other.dtype))
    504 

MemoryError: 

你能帮我弄清楚发生了什么以及可以做些什么吗？