scikit-learn · glemaitre · Aug 26, 2020 · Aug 19, 2020 · Aug 20, 2020 · Aug 20, 2020
diff --git a/sklearn/ensemble/_hist_gradient_boosting/grower.py b/sklearn/ensemble/_hist_gradient_boosting/grower.py
@@ -1,7 +1,7 @@
 """
 This module contains the TreeGrower class.
 
-TreeGrowee builds a regression tree fitting a Newton-Raphson step, based on
+TreeGrower builds a regression tree fitting a Newton-Raphson step, based on
 the gradients and hessians of the training data.
 """
 # Author: Nicolas Hug
@@ -33,20 +33,20 @@ class TreeNode:
     ----------
     depth : int
         The depth of the node, i.e. its distance from the root.
-    sample_indices : ndarray of unsigned int, shape (n_samples_at_node,)
+    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint
         The indices of the samples at the node.
     sum_gradients : float
         The sum of the gradients of the samples at the node.
     sum_hessians : float
         The sum of the hessians of the samples at the node.
-    parent : TreeNode or None, optional (default=None)
+    parent : TreeNode, default=None
         The parent of the node. None for root.
 
     Attributes
     ----------
     depth : int
         The depth of the node, i.e. its distance from the root.
-    sample_indices : ndarray of unsigned int, shape (n_samples_at_node,)
+    sample_indices : ndarray of shape (n_samples_at_node,), dtype=np.uint
         The indices of the samples at the node.
     sum_gradients : float
         The sum of the gradients of the samples at the node.
@@ -134,45 +134,50 @@ class TreeGrower:
 
     Parameters
     ----------
-    X_binned : ndarray of int, shape (n_samples, n_features)
+    X_binned : ndarray of shape (n_samples, n_features), dtype=np.uint8
         The binned input samples. Must be Fortran-aligned.
-    gradients : ndarray, shape (n_samples,)
+    gradients : ndarray of shape (n_samples,)
         The gradients of each training sample. Those are the gradients of the
         loss w.r.t the predictions, evaluated at iteration ``i - 1``.
-    hessians : ndarray, shape (n_samples,)
+    hessians : ndarray of shape (n_samples,)
         The hessians of each training sample. Those are the hessians of the
         loss w.r.t the predictions, evaluated at iteration ``i - 1``.
-    max_leaf_nodes : int or None, optional (default=None)
+    max_leaf_nodes : int, default=None
         The maximum number of leaves for each tree. If None, there is no
         maximum limit.
-    max_depth : int or None, optional (default=None)
+    max_depth : int, default=None
         The maximum depth of each tree. The depth of a tree is the number of
         edges to go from the root to the deepest leaf.
         Depth isn't constrained by default.
-    min_samples_leaf : int, optional (default=20)
+    min_samples_leaf : int, default=20
         The minimum number of samples per leaf.
-    min_gain_to_split : float, optional (default=0.)
+    min_gain_to_split : float, default=0.
         The minimum gain needed to split a node. Splits with lower gain will
         be ignored.
-    n_bins : int, optional (default=256)
+    n_bins : int, default=256
         The total number of bins, including the bin for missing values. Used
         to define the shape of the histograms.
-    n_bins_non_missing_ : array of uint32
+    n_bins_non_missing : ndarray, dtype=np.uint32, default=None
         For each feature, gives the number of bins actually used for
         non-missing values. For features with a lot of unique values, this
         is equal to ``n_bins - 1``. If it's an int, all features are
         considered to have the same number of bins. If None, all features
         are considered to have ``n_bins - 1`` bins.
-    has_missing_values : ndarray of bool or bool, optional (default=False)
+    has_missing_values : bool or ndarray, dtype=bool, default=False
         Whether each feature contains missing values (in the training data).
         If it's a bool, the same value is used for all features.
-    l2_regularization : float, optional (default=0)
+    monotonic_cst : array-like of shape (n_features,), dtype=int, default=None
+        Indicates the monotonic constraint to enforce on each feature. -1, 1
+        and 0 respectively correspond to a positive constraint, negative
+        constraint and no constraint. Read more in the :ref:`User Guide
+        <monotonic_cst_gbdt>`.
+    l2_regularization : float, default=0.
         The L2 regularization parameter.
-    min_hessian_to_split : float, optional (default=1e-3)
+    min_hessian_to_split : float, default=1e-3
         The minimum sum of hessians needed in each node. Splits that result in
         at least one child having a sum of hessians less than
         ``min_hessian_to_split`` are discarded.
-    shrinkage : float, optional (default=1)
+    shrinkage : float, default=1.
         The shrinkage parameter to apply to the leaves values, also known as
         learning rate.
     """