Score functions
The common approach to solve supervised learning tasks is to minimize the loss function :
 is the value of the loss function at the point
 is the weight of the th object
 is the regularization term.
 (mean squared error)
(L2 regularization)
Gradient boosting
Boosting is a method which builds a prediction model as an ensemble of weak learners .
In our case, is a decision tree. Trees are built sequentially and each next tree is built to approximate negative gradients of the loss function at predictions of the current ensemble:
Thus, it performs a gradient descent optimization of the function . The quality of the gradient approximation is measured by a score function .
Types of score functions
Finding the optimal tree structure
Let's suppose that it is required to find the structure for the tree of depth 1. The structure of such tree is determined by the index of some feature and a border value . Let be the value of the th feature on the th object and and be the values at leafs of . Then, equals to if and if . Now the goal is to find the best and in terms of the chosen score function.
For the L2 score function the formula takes the following form:
Let's denote and .
After expanding brackets and removing terms, which are constant in the optimization:
The latter argmax can be calculated by brute force search.
The L2 score function can be calculated for each leaf separately. This is not true for Cosine. The restrictions imposed on the growing policy are shown below.
SymmetricTree or Depthwise — S is calculated over all leaves:
 Lossguide — The leaves are updated one by one. Therefore, it is required to compute the gain of a split — the change of the score function after the split. The gain is calculated as follows:
Secondorder score functions
Let's apply the Taylor expansion to the loss function at the point :
 is the l2 regularization parameter
Since the first term is constant in optimization, the formula takes the following form after regrouping by leaves:
So, the optimal value of is:
The summation is over such that the object gets to the considered leaf. Then these optimal values of can be used instead of weighted averages of gradients ( and in the example above) in the same score functions.
CatBoost score functions
CatBoost provides the following score functions:
Score function  Description 

L2  Use the first derivatives during the calculation. 
Cosine (can not be used with the Lossguide tree growing policy)  
NewtonL2  Use the second derivatives during the calculation. This may improve the resulting quality of the model. 
NewtonCosine (can not be used with the Lossguide tree growing policy)  
LOOL2  Provide different heuristics of the L2 estimates 
SolarL2  
SatL2 
Score function  Description 

L2  Use the first derivatives during the calculation. 
Cosine (can not be used with the Lossguide tree growing policy)  
NewtonL2  Use the second derivatives during the calculation. This may improve the resulting quality of the model. 
NewtonCosine (can not be used with the Lossguide tree growing policy)  
LOOL2  Provide different heuristics of the L2 estimates 
SolarL2  
SatL2 
Usage
Use the corresponding parameter to set the score function during the training:
The supported score functions vary depending on the processing unit type:
GPU — All score types
CPU — Cosine, L2
Python package  R package  Commandline interface  Description 

score_function  score_function  scorefunction  The score type used to select the next split during the tree construction. Possible values:

Python package  R package  Commandline interface  Description 

score_function  score_function  scorefunction  The score type used to select the next split during the tree construction. Possible values:
