Building a random forest regression model

This example uses the "mtcars" dataset to create a random forest model to predict the value of carb (the number of carburetors).

1. Use RF_REGRESSOR to create the random forest model myRFRegressorModel using the mtcars training data. View the summary output of the model with GET_MODEL_SUMMARY:

   => 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)
   

2. Use PREDICT_RF_REGRESSOR to predict the number of carburetors:

   => 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)