Classifying data using SVM (support vector machine)

This SVM example uses a small data set named mtcars.

This SVM example uses a small data set named mtcars. The example shows how you can use the SVM_CLASSIFIER function to train the model to predict the value of am (the transmission type, where 0 = automatic and 1 = manual) using the PREDICT_SVM_CLASSIFIER function.

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

  1. Create the SVM model, named svm_class, using the mtcars_train training data.

    
=> SELECT SVM_CLASSIFIER('svm_class', 'mtcars_train', 'am', 'cyl, mpg, wt, hp, gear'
                          USING PARAMETERS exclude_columns='gear');
SVM_CLASSIFIER
----------------------------------------------------------------
Finished in 12 iterations.
Accepted Rows: 20  Rejected Rows: 0
(1 row)

  2. View the summary output of svm_class.

    
=> SELECT GET_MODEL_SUMMARY(USING PARAMETERS model_name='svm_class');
------------------------------------------------------------------------

=======
details
=======
predictor|coefficient
---------+-----------
Intercept| -0.02006
cyl      |  0.15367
mpg      |  0.15698
wt       | -1.78157
hp       |  0.00957

===========
call_string
===========
SELECT svm_classifier('public.svm_class', 'mtcars_train', '"am"', 'cyl, mpg, wt, hp, gear'
USING PARAMETERS exclude_columns='gear', C=1, max_iterations=100, epsilon=0.001);

===============
Additional Info
===============
Name              |Value
------------------+-----
accepted_row_count| 20
rejected_row_count|  0
iteration_count   | 12
(1 row)

  3. Create a new table, named svm_mtcars_predict. Populate this table with the prediction outputs you obtain from running the PREDICT_SVM_CLASSIFIER function on your test data.

    => CREATE TABLE svm_mtcars_predict AS
   (SELECT car_model, am, PREDICT_SVM_CLASSIFIER(cyl, mpg, wt, hp
                                            USING PARAMETERS model_name='svm_class')
                                            AS Prediction FROM mtcars_test);
CREATE TABLE

  4. View the results in the svm_mtcars_predict table.

    => SELECT * FROM svm_mtcars_predict;
car_model     | am | Prediction
------------- +----+------------
Toyota Corona |  0 |          1
Camaro Z28    |  0 |          0
Datsun 710    |  1 |          1
Valiant       |  0 |          0
Volvo 142E    |  1 |          1
AMC Javelin   |  0 |          0
Honda Civic   |  1 |          1
Hornet 4 Drive|  0 |          0
Maserati Bora |  1 |          1
Merc 280      |  0 |          0
Merc 450SL    |  0 |          0
Porsche 914-2 |  1 |          1
(12 rows)

  5. Evaluate the accuracy of the PREDICT_SVM_CLASSIFIER function, using the CONFUSION_MATRIX evaluation function.

    => SELECT CONFUSION_MATRIX(obs::int, pred::int USING PARAMETERS num_classes=2) OVER()
        FROM (SELECT am AS obs, Prediction AS pred FROM svm_mtcars_predict) AS prediction_output;
 class | 0 | 1 |                   comment
-------+---+---+---------------------------------------------
     0 | 6 | 1 |
     1 | 0 | 5 | Of 12 rows, 12 were used and 0 were ignored
(2 rows)

    In this case, PREDICT_SVM_CLASSIFIER correctly predicted that the cars with a value of 1 in the am column have a value of 1. No cars were incorrectly classified. Out of the seven cars which had a value of 0 in the am column, six were correctly predicted to have the value 0. One car was incorrectly classified as having the value 1.

See also