diff --git a/sklearn/ensemble/forest.py b/sklearn/ensemble/forest.py
index e550269001b..ea317c5e7e7 100644
--- a/sklearn/ensemble/forest.py
+++ b/sklearn/ensemble/forest.py
@@ -253,8 +253,10 @@ class BaseForest(six.with_metaclass(ABCMeta, BaseEnsemble,
 
         # Precompute some data
         X, y = check_arrays(X, y, sparse_format="dense")
-        if (getattr(X, "dtype", None) != DTYPE or X.ndim != 2):
-            X = array2d(X, dtype=DTYPE)
+        if ((getattr(X, "dtype", None) != DTYPE) or
+            (X.ndim != 2) or
+            (not X.flags.contiguous)):
+            X = array2d(X, dtype=DTYPE, order="C")
 
         n_samples, self.n_features_ = X.shape
 
diff --git a/sklearn/ensemble/gradient_boosting.py b/sklearn/ensemble/gradient_boosting.py
index 4b4ee8fa859..3a44f47f5aa 100644
--- a/sklearn/ensemble/gradient_boosting.py
+++ b/sklearn/ensemble/gradient_boosting.py
@@ -496,8 +496,7 @@ class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)):
         ----------
         X : array-like, shape = [n_samples, n_features]
             Training vectors, where n_samples is the number of samples
-            and n_features is the number of features. Use fortran-style
-            to avoid memory copies.
+            and n_features is the number of features.
 
         y : array-like, shape = [n_samples]
             Target values (integers in classification, real numbers in
@@ -511,9 +510,14 @@ class BaseGradientBoosting(six.with_metaclass(ABCMeta, BaseEnsemble)):
             Returns self.
         """
         # Check input
-        X, y = check_arrays(X, y, sparse_format='dense')
-        X = np.asfortranarray(X, dtype=DTYPE)
-        y = np.ravel(y, order='C')
+        X, y = check_arrays(X, y, sparse_format="dense")
+
+        if ((getattr(X, "dtype", None) != DTYPE) or
+            (X.ndim != 2) or
+            (not X.flags.contiguous)):
+            X = array2d(X, dtype=DTYPE, order="C")
+
+        y = np.ravel(y, order="C")
 
         # Check parameters
         n_samples, n_features = X.shape
@@ -816,8 +820,7 @@ class GradientBoostingClassifier(BaseGradientBoosting, ClassifierMixin):
         ----------
         X : array-like, shape = [n_samples, n_features]
             Training vectors, where n_samples is the number of samples
-            and n_features is the number of features. Use fortran-style
-            to avoid memory copies.
+            and n_features is the number of features.
 
         y : array-like, shape = [n_samples]
             Target values (integers in classification, real numbers in
@@ -1048,8 +1051,7 @@ class GradientBoostingRegressor(BaseGradientBoosting, RegressorMixin):
         ----------
         X : array-like, shape = [n_samples, n_features]
             Training vectors, where n_samples is the number of samples
-            and n_features is the number of features. Use fortran-style
-            to avoid memory copies.
+            and n_features is the number of features.
 
         y : array-like, shape = [n_samples]
             Target values (integers in classification, real numbers in
diff --git a/sklearn/tree/_tree.c b/sklearn/tree/_tree.c
index 814690e742c..0001b7e66d9 100644
--- a/sklearn/tree/_tree.c
+++ b/sklearn/tree/_tree.c
@@ -1,4 +1,4 @@
-/* Generated by Cython 0.20dev on Thu Jul 18 08:49:01 2013 */
+/* Generated by Cython 0.20dev on Thu Jul 18 10:24:33 2013 */
 
 #define PY_SSIZE_T_CLEAN
 #ifndef CYTHON_USE_PYLONG_INTERNALS
@@ -1320,11 +1320,12 @@ static void __Pyx_WriteUnraisable(const char *name, int clineno,
 
 static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
 
-#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1)
+#define __Pyx_BufPtrCContig2d(type, buf, i0, s0, i1, s1) ((type)((char*)buf + i0 * s0) + i1)
 static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); /*proto*/
 
 static void __Pyx_RaiseBufferFallbackError(void); /*proto*/
 
+#define __Pyx_BufPtrStrided2d(type, buf, i0, s0, i1, s1) (type)((char*)buf + i0 * s0 + i1 * s1)
 #define __Pyx_BufPtrStrided3d(type, buf, i0, s0, i1, s1, i2, s2) (type)((char*)buf + i0 * s0 + i1 * s1 + i2 * s2)
 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
 
@@ -6985,7 +6986,7 @@ static PyObject *__pyx_pf_7sklearn_4tree_5_tree_8Splitter_6__setstate__(CYTHON_U
 /* "sklearn/tree/_tree.pyx":818
  *         pass
  * 
- *     cdef void init(self, np.ndarray[DTYPE_t, ndim=2] X,             # <<<<<<<<<<<<<<
+ *     cdef void init(self, np.ndarray[DTYPE_t, ndim=2, mode="c"] X,             # <<<<<<<<<<<<<<
  *                          np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
  *                          DOUBLE_t* sample_weight):
  */
@@ -7024,7 +7025,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_8Splitter_init(struct __pyx_obj_7sklea
   __pyx_pybuffernd_y.rcbuffer = &__pyx_pybuffer_y;
   {
     __Pyx_BufFmt_StackElem __pyx_stack[1];
-    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 818; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_C_CONTIGUOUS, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 818; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   }
   __pyx_pybuffernd_X.diminfo[0].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_X.diminfo[0].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_X.diminfo[1].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_X.diminfo[1].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[1];
   {
@@ -7922,7 +7923,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
  *         cdef SIZE_t* features = self.features
  *         cdef SIZE_t n_features = self.n_features             # <<<<<<<<<<<<<<
  * 
- *         cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
+ *         cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
  */
   __pyx_t_3 = __pyx_v_self->__pyx_base.n_features;
   __pyx_v_n_features = __pyx_t_3;
@@ -7930,7 +7931,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
   /* "sklearn/tree/_tree.pyx":907
  *         cdef SIZE_t n_features = self.n_features
  * 
- *         cdef np.ndarray[DTYPE_t, ndim=2] X = self.X             # <<<<<<<<<<<<<<
+ *         cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X             # <<<<<<<<<<<<<<
  *         cdef SIZE_t max_features = self.max_features
  *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
  */
@@ -7938,7 +7939,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
   __Pyx_INCREF(__pyx_t_1);
   {
     __Pyx_BufFmt_StackElem __pyx_stack[1];
-    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)((PyArrayObject *)__pyx_t_1), &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 2, 0, __pyx_stack) == -1)) {
+    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)((PyArrayObject *)__pyx_t_1), &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_C_CONTIGUOUS, 2, 0, __pyx_stack) == -1)) {
       __pyx_v_X = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); __pyx_pybuffernd_X.rcbuffer->pybuffer.buf = NULL;
       {__pyx_filename = __pyx_f[0]; __pyx_lineno = 907; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     } else {__pyx_pybuffernd_X.diminfo[0].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_X.diminfo[0].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_X.diminfo[1].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_X.diminfo[1].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[1];
@@ -7949,7 +7950,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
 
   /* "sklearn/tree/_tree.pyx":908
  * 
- *         cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
+ *         cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
  *         cdef SIZE_t max_features = self.max_features             # <<<<<<<<<<<<<<
  *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
  *         cdef unsigned int* random_state = &self.rand_r_state
@@ -7958,7 +7959,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
   __pyx_v_max_features = __pyx_t_3;
 
   /* "sklearn/tree/_tree.pyx":909
- *         cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
+ *         cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
  *         cdef SIZE_t max_features = self.max_features
  *         cdef SIZE_t min_samples_leaf = self.min_samples_leaf             # <<<<<<<<<<<<<<
  *         cdef unsigned int* random_state = &self.rand_r_state
@@ -8127,7 +8128,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
           __pyx_t_6 = __pyx_v_current_feature;
           __pyx_t_7 = (__pyx_v_samples[__pyx_v_p]);
           __pyx_t_8 = __pyx_v_current_feature;
-          __pyx_t_9 = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_5, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_6, __pyx_pybuffernd_X.diminfo[1].strides)) <= ((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_7, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_8, __pyx_pybuffernd_X.diminfo[1].strides)) + 1.e-7)) != 0);
+          __pyx_t_9 = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_5, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_6, __pyx_pybuffernd_X.diminfo[1].strides)) <= ((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_7, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_8, __pyx_pybuffernd_X.diminfo[1].strides)) + 1.e-7)) != 0);
           __pyx_t_10 = __pyx_t_9;
         } else {
           __pyx_t_10 = __pyx_t_4;
@@ -8274,7 +8275,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
           __pyx_t_12 = __pyx_v_current_feature;
           __pyx_t_13 = (__pyx_v_samples[__pyx_v_p]);
           __pyx_t_14 = __pyx_v_current_feature;
-          __pyx_v_current_threshold = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_11, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_12, __pyx_pybuffernd_X.diminfo[1].strides)) + (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_14, __pyx_pybuffernd_X.diminfo[1].strides))) / 2.0);
+          __pyx_v_current_threshold = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_11, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_12, __pyx_pybuffernd_X.diminfo[1].strides)) + (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_13, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_14, __pyx_pybuffernd_X.diminfo[1].strides))) / 2.0);
 
           /* "sklearn/tree/_tree.pyx":972
  * 
@@ -8285,7 +8286,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
  */
           __pyx_t_15 = (__pyx_v_samples[__pyx_v_p]);
           __pyx_t_16 = __pyx_v_current_feature;
-          __pyx_t_9 = ((__pyx_v_current_threshold == (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_15, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_16, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
+          __pyx_t_9 = ((__pyx_v_current_threshold == (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_15, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_16, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
           if (__pyx_t_9) {
 
             /* "sklearn/tree/_tree.pyx":973
@@ -8297,7 +8298,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
  */
             __pyx_t_17 = (__pyx_v_samples[(__pyx_v_p - 1)]);
             __pyx_t_18 = __pyx_v_current_feature;
-            __pyx_v_current_threshold = (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_17, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_18, __pyx_pybuffernd_X.diminfo[1].strides));
+            __pyx_v_current_threshold = (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_17, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_18, __pyx_pybuffernd_X.diminfo[1].strides));
             goto __pyx_L12;
           }
           __pyx_L12:;
@@ -8432,7 +8433,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
  */
       __pyx_t_3 = (__pyx_v_samples[__pyx_v_p]);
       __pyx_t_19 = __pyx_v_best_feature;
-      __pyx_t_9 = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_3, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_19, __pyx_pybuffernd_X.diminfo[1].strides)) <= __pyx_v_best_threshold) != 0);
+      __pyx_t_9 = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_3, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_19, __pyx_pybuffernd_X.diminfo[1].strides)) <= __pyx_v_best_threshold) != 0);
       if (__pyx_t_9) {
 
         /* "sklearn/tree/_tree.pyx":994
@@ -8512,7 +8513,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
  *         feature[0] = best_feature
  *         threshold[0] = best_threshold             # <<<<<<<<<<<<<<
  * 
- * cdef void sort(np.ndarray[DTYPE_t, ndim=2] X, SIZE_t current_feature,
+ * cdef void sort(np.ndarray[DTYPE_t, ndim=2, mode="c"] X, SIZE_t current_feature,
  */
   (__pyx_v_threshold[0]) = __pyx_v_best_threshold;
 
@@ -8536,7 +8537,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_12BestSplitter_node_split(struct __pyx
 /* "sklearn/tree/_tree.pyx":1008
  *         threshold[0] = best_threshold
  * 
- * cdef void sort(np.ndarray[DTYPE_t, ndim=2] X, SIZE_t current_feature,             # <<<<<<<<<<<<<<
+ * cdef void sort(np.ndarray[DTYPE_t, ndim=2, mode="c"] X, SIZE_t current_feature,             # <<<<<<<<<<<<<<
  *                SIZE_t* samples, SIZE_t length):
  *     """In-place sorting of samples[start:end] using
  */
@@ -8572,7 +8573,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
   __pyx_pybuffernd_X.rcbuffer = &__pyx_pybuffer_X;
   {
     __Pyx_BufFmt_StackElem __pyx_stack[1];
-    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_STRIDES, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    if (unlikely(__Pyx_GetBufferAndValidate(&__pyx_pybuffernd_X.rcbuffer->pybuffer, (PyObject*)__pyx_v_X, &__Pyx_TypeInfo_nn___pyx_t_7sklearn_4tree_5_tree_DTYPE_t, PyBUF_FORMAT| PyBUF_C_CONTIGUOUS, 2, 0, __pyx_stack) == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   }
   __pyx_pybuffernd_X.diminfo[0].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_X.diminfo[0].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_X.diminfo[1].strides = __pyx_pybuffernd_X.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_X.diminfo[1].shape = __pyx_pybuffernd_X.rcbuffer->pybuffer.shape[1];
 
@@ -8695,7 +8696,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
  */
     __pyx_t_2 = __pyx_v_tmp;
     __pyx_t_3 = __pyx_v_current_feature;
-    __pyx_v_tmp_value = (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_2, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_3, __pyx_pybuffernd_X.diminfo[1].strides));
+    __pyx_v_tmp_value = (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_2, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_3, __pyx_pybuffernd_X.diminfo[1].strides));
 
     /* "sklearn/tree/_tree.pyx":1031
  * 
@@ -8747,7 +8748,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
         __pyx_t_5 = __pyx_v_current_feature;
         __pyx_t_6 = (__pyx_v_samples[__pyx_v_child]);
         __pyx_t_7 = __pyx_v_current_feature;
-        __pyx_t_8 = (((*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_4, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_5, __pyx_pybuffernd_X.diminfo[1].strides)) > (*__Pyx_BufPtrStrided2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_6, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_7, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
+        __pyx_t_8 = (((*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_4, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_5, __pyx_pybuffernd_X.diminfo[1].strides)) > (*__Pyx_BufPtrCContig2d(__pyx_t_7sklearn_4tree_5_tree_DTYPE_t *, __pyx_pybuffernd_X.rcbuffer->pybuffer.buf, __pyx_t_6, __pyx_pybuffernd_X.diminfo[0].strides, __pyx_t_7, __pyx_pybuffernd_X.diminfo[1].strides))) != 0);
         __pyx_t_9 = __pyx_t_8;
       } else {
         __pyx_t_9 = __pyx_t_1;
@@ -8775,7 +8776,7 @@ static void __pyx_f_7sklearn_4tree_5_tree_sort(PyArrayObject *__pyx_v_X, __pyx_t
  */
       __pyx_t_10 = (__pyx_v_samples[__pyx_v_child]);
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-  __pyx_v_splitter = ((struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *)__pyx_t_2);
-  __pyx_t_2 = 0;
+  __pyx_t_3 = ((PyObject *)__pyx_v_self->splitter);
+  __Pyx_INCREF(__pyx_t_3);
+  __pyx_v_splitter = ((struct __pyx_obj_7sklearn_4tree_5_tree_Splitter *)__pyx_t_3);
+  __pyx_t_3 = 0;
 
   /* "sklearn/tree/_tree.pyx":1456
  *         # Recursive partition (without actual recursion)
@@ -12469,8 +12484,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  * 
  */
   while (1) {
-    __pyx_t_6 = ((__pyx_v_stack_n_values > 0) != 0);
-    if (!__pyx_t_6) break;
+    __pyx_t_4 = ((__pyx_v_stack_n_values > 0) != 0);
+    if (!__pyx_t_4) break;
 
     /* "sklearn/tree/_tree.pyx":1484
  * 
@@ -12542,8 +12557,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  *                        (n_node_samples < self.min_samples_split) or
  *                        (n_node_samples < 2 * self.min_samples_leaf))
  */
-    __pyx_t_6 = (__pyx_v_depth >= __pyx_v_self->max_depth);
-    if (!__pyx_t_6) {
+    __pyx_t_4 = (__pyx_v_depth >= __pyx_v_self->max_depth);
+    if (!__pyx_t_4) {
 
       /* "sklearn/tree/_tree.pyx":1494
  *             n_node_samples = end - start
@@ -12552,8 +12567,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  *                        (n_node_samples < 2 * self.min_samples_leaf))
  * 
  */
-      __pyx_t_4 = (__pyx_v_n_node_samples < __pyx_v_self->min_samples_split);
-      if (!__pyx_t_4) {
+      __pyx_t_5 = (__pyx_v_n_node_samples < __pyx_v_self->min_samples_split);
+      if (!__pyx_t_5) {
 
         /* "sklearn/tree/_tree.pyx":1495
  *             is_leaf = ((depth >= self.max_depth) or
@@ -12562,16 +12577,16 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  * 
  *             splitter.node_reset(start, end, &impurity)
  */
-        __pyx_t_7 = (__pyx_v_n_node_samples < (2 * __pyx_v_self->min_samples_leaf));
-        __pyx_t_10 = __pyx_t_7;
+        __pyx_t_6 = (__pyx_v_n_node_samples < (2 * __pyx_v_self->min_samples_leaf));
+        __pyx_t_10 = __pyx_t_6;
       } else {
-        __pyx_t_10 = __pyx_t_4;
+        __pyx_t_10 = __pyx_t_5;
       }
-      __pyx_t_4 = __pyx_t_10;
+      __pyx_t_5 = __pyx_t_10;
     } else {
-      __pyx_t_4 = __pyx_t_6;
+      __pyx_t_5 = __pyx_t_4;
     }
-    __pyx_v_is_leaf = __pyx_t_4;
+    __pyx_v_is_leaf = __pyx_t_5;
 
     /* "sklearn/tree/_tree.pyx":1497
  *                        (n_node_samples < 2 * self.min_samples_leaf))
@@ -12590,12 +12605,12 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  *             if not is_leaf:
  */
     if (!__pyx_v_is_leaf) {
-      __pyx_t_4 = (__pyx_v_impurity == 0.0);
-      __pyx_t_6 = __pyx_t_4;
+      __pyx_t_5 = (__pyx_v_impurity == 0.0);
+      __pyx_t_4 = __pyx_t_5;
     } else {
-      __pyx_t_6 = __pyx_v_is_leaf;
+      __pyx_t_4 = __pyx_v_is_leaf;
     }
-    __pyx_v_is_leaf = __pyx_t_6;
+    __pyx_v_is_leaf = __pyx_t_4;
 
     /* "sklearn/tree/_tree.pyx":1500
  *             is_leaf = is_leaf or (impurity == 0.0)
@@ -12604,8 +12619,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  *                 splitter.node_split(&pos, &feature, &threshold)
  *                 is_leaf = is_leaf or (pos >= end)
  */
-    __pyx_t_6 = ((!(__pyx_v_is_leaf != 0)) != 0);
-    if (__pyx_t_6) {
+    __pyx_t_4 = ((!(__pyx_v_is_leaf != 0)) != 0);
+    if (__pyx_t_4) {
 
       /* "sklearn/tree/_tree.pyx":1501
  * 
@@ -12624,12 +12639,12 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  *             node_id = self._add_node(parent, is_left, is_leaf, feature,
  */
       if (!__pyx_v_is_leaf) {
-        __pyx_t_6 = (__pyx_v_pos >= __pyx_v_end);
-        __pyx_t_4 = __pyx_t_6;
+        __pyx_t_4 = (__pyx_v_pos >= __pyx_v_end);
+        __pyx_t_5 = __pyx_t_4;
       } else {
-        __pyx_t_4 = __pyx_v_is_leaf;
+        __pyx_t_5 = __pyx_v_is_leaf;
       }
-      __pyx_v_is_leaf = __pyx_t_4;
+      __pyx_v_is_leaf = __pyx_t_5;
       goto __pyx_L10;
     }
     __pyx_L10:;
@@ -12650,8 +12665,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  *                 # Don't store value for internal nodes
  *                 splitter.node_value(self.value + node_id * self.value_stride)
  */
-    __pyx_t_4 = (__pyx_v_is_leaf != 0);
-    if (__pyx_t_4) {
+    __pyx_t_5 = (__pyx_v_is_leaf != 0);
+    if (__pyx_t_5) {
 
       /* "sklearn/tree/_tree.pyx":1509
  *             if is_leaf:
@@ -12672,8 +12687,8 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
  *                     stack_capacity *= 2
  *                     stack = <SIZE_t*> realloc(stack, stack_capacity * sizeof(SIZE_t))
  */
-      __pyx_t_4 = (((__pyx_v_stack_n_values + 10) > __pyx_v_stack_capacity) != 0);
-      if (__pyx_t_4) {
+      __pyx_t_5 = (((__pyx_v_stack_n_values + 10) > __pyx_v_stack_capacity) != 0);
+      if (__pyx_t_5) {
 
         /* "sklearn/tree/_tree.pyx":1513
  *             else:
@@ -12833,7 +12848,7 @@ static PyObject *__pyx_f_7sklearn_4tree_5_tree_4Tree_build(struct __pyx_obj_7skl
   __Pyx_XDECREF(__pyx_t_1);
   __Pyx_XDECREF(__pyx_t_2);
   __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_XDECREF(__pyx_t_7);
   __Pyx_AddTraceback("sklearn.tree._tree.Tree.build", __pyx_clineno, __pyx_lineno, __pyx_filename);
   __pyx_r = 0;
   __pyx_L0:;
diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx
index 8f3ff555f19..268e724f4ec 100644
--- a/sklearn/tree/_tree.pyx
+++ b/sklearn/tree/_tree.pyx
@@ -815,7 +815,7 @@ cdef class Splitter:
     def __setstate__(self, d):
         pass
 
-    cdef void init(self, np.ndarray[DTYPE_t, ndim=2] X,
+    cdef void init(self, np.ndarray[DTYPE_t, ndim=2, mode="c"] X,
                          np.ndarray[DOUBLE_t, ndim=2, mode="c"] y,
                          DOUBLE_t* sample_weight):
         """Initialize the splitter."""
@@ -904,7 +904,7 @@ cdef class BestSplitter(Splitter):
         cdef SIZE_t* features = self.features
         cdef SIZE_t n_features = self.n_features
 
-        cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
+        cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
         cdef SIZE_t max_features = self.max_features
         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
         cdef unsigned int* random_state = &self.rand_r_state
@@ -1005,7 +1005,7 @@ cdef class BestSplitter(Splitter):
         feature[0] = best_feature
         threshold[0] = best_threshold
 
-cdef void sort(np.ndarray[DTYPE_t, ndim=2] X, SIZE_t current_feature,
+cdef void sort(np.ndarray[DTYPE_t, ndim=2, mode="c"] X, SIZE_t current_feature,
                SIZE_t* samples, SIZE_t length):
     """In-place sorting of samples[start:end] using
       X[sample[i], current_feature] as key."""
@@ -1066,7 +1066,7 @@ cdef class RandomSplitter(Splitter):
         cdef SIZE_t* features = self.features
         cdef SIZE_t n_features = self.n_features
 
-        cdef np.ndarray[DTYPE_t, ndim=2] X = self.X
+        cdef np.ndarray[DTYPE_t, ndim=2, mode="c"] X = self.X
         cdef SIZE_t max_features = self.max_features
         cdef SIZE_t min_samples_leaf = self.min_samples_leaf
         cdef unsigned int* random_state = &self.rand_r_state
@@ -1427,8 +1427,8 @@ cdef class Tree:
                       np.ndarray sample_weight=None):
         """Build a decision tree from the training set (X, y)."""
         # Prepare data before recursive partitioning
-        if X.dtype != DTYPE:
-            X = np.asarray(X, dtype=DTYPE)
+        if X.dtype != DTYPE or not X.flags.contiguous:
+            X = np.asarray(X, dtype=DTYPE, order="C")
 
         if y.dtype != DOUBLE or not y.flags.contiguous:
             y = np.asarray(y, dtype=DOUBLE, order="C")
diff --git a/sklearn/tree/tree.py b/sklearn/tree/tree.py
index 545f8b39dc9..a5c51c3eb42 100644
--- a/sklearn/tree/tree.py
+++ b/sklearn/tree/tree.py
@@ -129,8 +129,10 @@ class BaseDecisionTree(six.with_metaclass(ABCMeta, BaseEstimator,
             X, y = check_arrays(X, y)
         random_state = check_random_state(self.random_state)
 
-        if (getattr(X, "dtype", None) != DTYPE or X.ndim != 2):
-            X = array2d(X, dtype=DTYPE)
+        if ((getattr(X, "dtype", None) != DTYPE) or
+            (X.ndim != 2) or
+            (not X.flags.contiguous)):
+            X = array2d(X, dtype=DTYPE, order="C")
 
         n_samples, self.n_features_ = X.shape
         is_classification = isinstance(self, ClassifierMixin)