XGBoost for regression

The following XGBoost functions create and perform predictions with a regression model:.

XGBoost (eXtreme Gradient Boosting) is a popular supervised-learning algorithm used for regression and classification on large datasets. It uses sequentially-built shallow decision trees to provide accurate results and a highly-scalable training method that avoids overfitting.

The following XGBoost functions create and perform predictions with a regression model:

Example

This example uses a small data set named "mtcars", which contains design and performance data for 32 automobiles from 1973-1974, and creates an XGBoost regression model to predict the value of the variable carb (the number of carburetors).

Before you begin the example, load the Machine Learning sample data.

  1. Use XGB_REGRESSOR to create the XGBoost regression model xgb_cars from the mtcars dataset:

    => SELECT XGB_REGRESSOR ('xgb_cars', 'mtcars', 'carb', 'mpg, cyl, hp, drat, wt'
    USING PARAMETERS learning_rate=0.5);
 XGB_REGRESSOR
---------------
 Finished
(1 row)

    You can then view a summary of the model with GET_MODEL_SUMMARY:

    
=> SELECT GET_MODEL_SUMMARY(USING PARAMETERS model_name='xgb_cars');
                  GET_MODEL_SUMMARY
------------------------------------------------------
===========
call_string
===========
xgb_regressor('public.xgb_cars', 'mtcars', '"carb"', 'mpg, cyl, hp, drat, wt'
USING PARAMETERS exclude_columns='', max_ntree=10, max_depth=5, nbins=32, objective=squarederror,
split_proposal_method=global, epsilon=0.001, learning_rate=0.5, min_split_loss=0, weight_reg=0, sampling_size=1)

=======
details
=======
predictor|      type
---------+----------------
   mpg   |float or numeric
   cyl   |      int
   hp    |      int
  drat   |float or numeric
   wt    |float or numeric

===============
Additional Info
===============
       Name       |Value
------------------+-----
    tree_count    | 10
rejected_row_count|  0
accepted_row_count| 32

(1 row)

  2. Use PREDICT_XGB_REGRESSOR to predict the number of carburetors:

    => SELECT carb, PREDICT_XGB_REGRESSOR (mpg,cyl,hp,drat,wt USING PARAMETERS model_name='xgb_cars') FROM mtcars;
 carb | PREDICT_XGB_REGRESSOR
------+-----------------------
    4 |      4.00335213618023
    2 |       2.0038188946536
    6 |      5.98866003194438
    1 |      1.01774386191546
    2 |       1.9959801016274
    2 |       2.0038188946536
    4 |      3.99545403625739
    8 |      7.99211056556231
    2 |      1.99291901733151
    3 |       2.9975688946536
    3 |       2.9975688946536
    1 |      1.00320357711227
    2 |       2.0038188946536
    4 |      3.99545403625739
    4 |      4.00124134679445
    1 |      1.00759516721382
    4 |      3.99700517763435
    4 |      3.99580193056138
    4 |      4.00009088187525
    3 |       2.9975688946536
    2 |      1.98625064560888
    1 |      1.00355294416998
    2 |      2.00666247039502
    1 |      1.01682931210169
    4 |      4.00124134679445
    1 |      1.01007809485918
    2 |      1.98438405824605
    4 |      3.99580193056138
    2 |      1.99291901733151
    4 |      4.00009088187525
    2 |       2.0038188946536
    1 |      1.00759516721382
(32 rows)