From 91c513a0c1594fd2c81b7a0cd0dca12328181a02 Mon Sep 17 00:00:00 2001 From: tqchen Date: Mon, 29 Apr 2019 17:50:46 -0700 Subject: [PATCH] fix doc --- doc/tutorials/model.rst | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/doc/tutorials/model.rst b/doc/tutorials/model.rst index a4c2cf98c..4720e7250 100644 --- a/doc/tutorials/model.rst +++ b/doc/tutorials/model.rst @@ -72,8 +72,7 @@ Decision Tree Ensembles *********************** Now that we have introduced the elements of supervised learning, let us get started with real trees. To begin with, let us first learn about the model choice of XGBoost: **decision tree ensembles**. -The tree ensemble model consists of a set of classification and regression trees (CART). Here's a simple example of a CART -that classifies whether someone will like computer games. +The tree ensemble model consists of a set of classification and regression trees (CART). Here's a simple example of a CART that classifies whether someone will like a hypothetical computer game X. .. image:: https://raw.githubusercontent.com/dmlc/web-data/master/xgboost/model/cart.png :width: 100% @@ -257,7 +256,7 @@ A left to right scan is sufficient to calculate the structure score of all possi .. note:: Limitation of additive tree learning - Since it is intractable to enumerate all possible tree structures, we add one split at a time. This approach works well most of the time, but there are some edge cases that fail due to this approach. For those edge cases, training results in a degenerate model because we consider only one feature dimension at a time. See `Can Gradient Boosting Learn Simple Arithmetic? `_ for an example. + Since it is intractable to enumerate all possible tree structures, we add one split at a time. This approach works well most of the time, but there are some edge cases that fail due to this approach. For those edge cases, training results in a degenerate model because we consider only one feature dimension at a time. See `Can Gradient Boosting Learn Simple Arithmetic? `_ for an example. ********************** Final words on XGBoost