DOC Update parameter description in sklearn/tree/_splitter.pyx
@@ -82,6 +82,10 @@ cdef class Splitter:
random_state : object
The user inputted random state to be used for pseudo-randomness
+
+ presort : bint
+ Indicate whether to presort the data to speed up the finding of best
+ splits in fitting.
"""
self.criterion = criterion
@@ -131,13 +135,16 @@ cdef class Splitter:
X : object
This contains the inputs. Usually it is a 2d numpy array.
- y : numpy.ndarray, dtype=DOUBLE_t
+ y : ndarray, dtype=DOUBLE_t
This is the vector of targets, or true labels, for the samples
- sample_weight : numpy.ndarray, dtype=DOUBLE_t (optional)
+ sample_weight : DOUBLE_t*
The weights of the samples, where higher weighted samples are fit
closer than lower weight samples. If not provided, all samples
are assumed to have uniform weight.
+
+ X_idx_sorted : ndarray, default=None
+ The indexes of the sorted training input samples
"""
self.rand_r_state = self.random_state.randint(0, RAND_R_MAX)
@@ -195,7 +202,7 @@ cdef class Splitter:
The index of the first sample to consider
end : SIZE_t
The index of the last sample to consider
- weighted_n_node_samples : numpy.ndarray, dtype=double pointer
+ weighted_n_node_samples : ndarray, dtype=double pointer
The total weight of those samples
"""
