Building a Random Forest for Regression Model

This random forest example uses a small data set named mtcars. The example shows you how to build a model to predict the value of carb (the number of carburetors). It uses the given values of all the other features in the data set.

Before you begin the example, make sure that you have loaded the Machine Learning sample data.

Create the random forest model, named myRFRegressorModel, using the mtcars_train training data. View the summary output of the model.

=> SELECT RF_REGRESSOR ('myRFRegressorModel', 'mtcars', 'carb', 'mpg, cyl, hp, drat, wt' USING PARAMETERS
ntree=100, sampling_size=0.3);
RF_REGRESSOR
--------------
Finished
(1 row)


=> SELECT GET_MODEL_SUMMARY(USING PARAMETERS model_name='myRFRegressorModel');
--------------------------------------------------------------------------------
===========
call_string
===========
SELECT rf_regressor('public.myRFRegressorModel', 'mtcars', '"carb"', 'mpg, cyl, hp, drat, wt' 
USING PARAMETERS exclude_columns='', ntree=100, mtry=1, sampling_size=0.3, max_depth=5, max_breadth=32, 
min_leaf_size=5, min_info_gain=0, nbins=32);

=======
details
=======
predictor|type
---------+-----
mpg      |float
cyl      | int
hp       | int
drat     |float
wt       |float
===============
Additional Info
===============
Name              |Value
------------------+-----
tree_count        | 100
rejected_row_count|  0
accepted_row_count| 32
(1 row)

Use PREDICT_RF_REGRESSOR to view the probability of the classes:

=> SELECT PREDICT_RF_REGRESSOR (mpg,cyl,hp,drat,wt 
USING PARAMETERS model_name='myRFRegressorModel')FROM mtcars;
PREDICT_RF_REGRESSOR
----------------------
2.94774203574204
2.6954087024087
2.6954087024087
2.89906346431346
2.97688489288489
2.97688489288489
2.7086587024087
2.92078965478965
2.97688489288489
2.7086587024087
2.95621822621823
2.82255155955156
2.7086587024087
2.7086587024087
2.85650394050394
2.85650394050394
2.97688489288489
2.95621822621823
2.6954087024087
2.6954087024087
2.84493251193251
2.97688489288489
2.97688489288489
2.8856467976468
2.6954087024087
2.92078965478965
2.97688489288489
2.97688489288489
2.7934087024087
2.7934087024087
2.7086587024087
2.72469441669442
(32 rows)

Building a Random Forest for Regression Model

See Also