Digging into the tree structure#

mlinsights.mltree.predict_leaves (model, X)

Returns the leave every observations of X falls into.

mlinsights.mltree.tree_structure.tree_find_common_node (tree, i, j, parents = None)

Finds the common node to nodes i and j.

mlinsights.mltree.tree_structure.tree_find_path_to_root (tree, i, parents = None)

Lists nodes involved into the path to find node i.

mlinsights.mltree.tree_structure.tree_node_parents (tree)

Returns a dictionary {node_id: parent_id}.

mlinsights.mltree.tree_node_range (tree, i, parents = None)

Determines the ranges for a node all dimensions. nan means infinity.

mlinsights.mltree.tree_leave_index (model)

Returns the indices of every leave in a tree.

mlinsights.mltree.tree_leave_neighbors (model)

The function determines which leaves are neighbors. The method uses some memory as it creates creates a grid of the feature spaces, each split multiplies the number of cells by two.

Experiments, exercise#

mlinsights.mltree.digitize2tree (bins, right = False)

Builds a decision tree which returns the same result as lambda x: numpy.digitize(x, bins, right=right) (see numpy.digitize).