Classifying data using random forest

This random forest example uses a data set named iris. The example contains four variables that measure various parts of the iris flower to predict its species.

Before you begin the example, make sure that you have followed the steps in Downloading the machine learning example data.

1. Use RF_CLASSIFIER to create the random forest model, named rf_iris, using the iris data. View the summary output of the model with GET_MODEL_SUMMARY:

   => SELECT RF_CLASSIFIER ('rf_iris', 'iris', 'Species', 'Sepal_Length, Sepal_Width, Petal_Length, Petal_Width'
   USING PARAMETERS ntree=100, sampling_size=0.5);
   
           RF_CLASSIFIER
   ----------------------------
   Finished training
   
   (1 row)
   
   
   => SELECT GET_MODEL_SUMMARY(USING PARAMETERS model_name='rf_iris');
   ------------------------------------------------------------------------
   ===========
   call_string
   ===========
   SELECT rf_classifier('public.rf_iris', 'iris', '"species"', 'Sepal_Length, Sepal_Width, Petal_Length,
   Petal_Width' USING PARAMETERS exclude_columns='', ntree=100, mtry=2, sampling_size=0.5, max_depth=5,
   max_breadth=32, min_leaf_size=1, min_info_gain=0, nbins=32);
   
   =======
   details
   =======
   predictor   |type
   ------------+-----
   sepal_length|float
   sepal_width |float
   petal_length|float
   petal_width |float
   
   ===============
   Additional Info
   ===============
   Name              |Value
   ------------------+-----
   tree_count        | 100
   rejected_row_count|  0
   accepted_row_count| 150
   (1 row)
   

2. Apply the classifier to the test data with PREDICT_RF_CLASSIFIER:

   => SELECT PREDICT_RF_CLASSIFIER (Sepal_Length, Sepal_Width, Petal_Length, Petal_Width
                                     USING PARAMETERS model_name='rf_iris') FROM iris1;
   
   PREDICT_RF_CLASSIFIER
   -----------------------
   setosa
   setosa
   setosa
   .
   .
   .
   versicolor
   versicolor
   versicolor
   .
   .
   .
   virginica
   virginica
   virginica
   .
   .
   .
   (90 rows)
   

3. Use PREDICT_RF_CLASSIFIER_CLASSES to view the probability of each class:

   => SELECT PREDICT_RF_CLASSIFIER_CLASSES(Sepal_Length, Sepal_Width, Petal_Length, Petal_Width
                                  USING PARAMETERS model_name='rf_iris') OVER () FROM iris1;
   predicted  |    probability
   -----------+-------------------
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |                 1
   setosa     |              0.99
   .
   .
   .
   (90 rows)