Hi,
I'm new to scikit-learn. I'm trying use preprocessing.OneHotEncoder to
encode my training and test data. After encoding I tried to train Random
forest classifier using that data. But I get the following error when
fitting.
(Here the error trace)
99 model.fit(X_train, y_train) 100 preds =
model.predict_proba(X_cv)[:, 1] 101
C:\Python27\lib\site-packages\sklearn\ensemble\forest.pyc in fit(self,
X, y, sample_weight) 288 289 # Precompute some data-->
290 X, y = check_arrays(X, y, sparse_format="dense") 291
if (getattr(X, "dtype", None) != DTYPE or 292
X.ndim != 2 or
C:\Python27\lib\site-packages\sklearn\utils\validation.pyc in
check_arrays(*arrays, **options) 200 array =
array.tocsc() 201 elif sparse_format == 'dense':-->
202 raise TypeError('A sparse matrix was passed,
but dense ' 203 'data is
required. Use X.toarray() to ' 204
'convert to a dense numpy array.')
TypeError: A sparse matrix was passed, but dense data is required. Use
X.toarray() to convert to a dense numpy array.
I tried to convert the sparse matrix into dense using X.toarray() and
X.todense()
But when I do that, I get the following error trace.
99 model.fit(X_train.toarray(), y_train) 100 preds
= model.predict_proba(X_cv)[:, 1] 101
C:\Python27\lib\site-packages\scipy\sparse\compressed.pyc in
toarray(self) 548 549 def toarray(self):--> 550
return self.tocoo(copy=False).toarray() 551 552
##############################################################
C:\Python27\lib\site-packages\scipy\sparse\coo.pyc in toarray(self)
236 237 def toarray(self):--> 238 B =
np.zeros(self.shape, dtype=self.dtype) 239 M,N = self.shape
240 coo_todense(M, N, self.nnz, self.row, self.col,
self.data, B.ravel())
ValueError: array is too big.
Can anyone help me to fix this.
Thank you
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