Hendrik/xgboost
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

add plot

Browse Source
This commit is contained in:
hetong007
2015-08-23 16:28:24 -07:00
parent d5d48560a7
commit 5196458305
3 changed files with 2 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

BIN
doc/img/cart.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 175 KiB

BIN
doc/img/twocart.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 100 KiB

4
doc/model.md
View File

@@ -47,7 +47,7 @@ where ``$ L $`` is the loss function, and ``$ \Omega $`` is the regularization t
The boosting trees model is a set of classification and regression trees. Here's a simple example of such a model: The boosting trees model is a set of classification and regression trees. Here's a simple example of such a model:
![CART]() ![CART](img/cart.png)
We classify the members in thie family into different leaves, and assign them the score on corresponding leaf. We classify the members in thie family into different leaves, and assign them the score on corresponding leaf.
@@ -55,7 +55,7 @@ We classify the members in thie family into different leaves, and assign them th
However a single CART model is not so strong in practice. How about predict with more trees? However a single CART model is not so strong in practice. How about predict with more trees?
![TwoCART]() ![TwoCART](img/twocart.png)
Now we are predicting with two trees, by predict on each tree individually and then sum the scores up. Mathematically, we can write our model into the form Now we are predicting with two trees, by predict on each tree individually and then sum the scores up. Mathematically, we can write our model into the form