6.7.1.5 Score a Classification Model

After generating predictions and explanations, you can score the model to assess its reliability. For a list of scoring metrics you can use with classification models, see Classification Metrics. For this use case, you use the test dataset for validation. In a real-world use case, you should use a separate validation dataset that has the target column and ground truth values for the scoring validation. You should also use a larger number of records for training and validation to get a valid score.

Before You Begin

Complete the following tasks:

• Prepare Data for a Classification Model

• Train a Classification Model

• Generate Predictions for a Classification Model

• Query Model Explanation and Generate Prediction Explanations for a Classification Model

Score Model

1. If not already done, load the model. You can use the session variable for the model that is valid for the duration of the connection. Alternatively, you can use the model handle previously set. For the option to set the user name, you can set it to NULL.

   The following example uses the session variable.

   mysql> CALL sys.ML_MODEL_LOAD(@model, NULL);

   The following example uses the model handle.

   mysql> CALL sys.ML_MODEL_LOAD('classification_use_case', NULL);

2. Score the model with the ML_SCORE routine and use the accuracy metric.

   mysql> CALL sys.ML_SCORE(table_name, target_column_name, model_handle, metric, score, [options]);

   Replace table_name, target_column_name, model_handle, metric, score with your own values.

   The following example runs ML_SCORE on the testing dataset previously created.

   mysql> CALL sys.ML_SCORE('classification_data.Loan_Testing', 'Approved', @model, 'accuracy', @classification_score, NULL);

   Where:

   • classification_data.Loan_Testing is the fully qualified name of the validation dataset.

   • Approved is the target column name with ground truth values.

   • @model is the session variable for the model handle.

   • accuracy is the selected scoring metric.

   • @classification_score is the session variable name for the score value.

   • NULL means that no other options are defined for the routine.

3. Retrieve the score by querying the @classification_score session variable.

   mysql> SELECT @classification_score;
   +-----------------------+
   | @classification_score |
   +-----------------------+
   |     0.800000011920929 |
   +-----------------------+
   1 row in set (0.0431 sec)

4. If done working with the model, unload it with the ML_MODEL_UNLOAD routine.

   mysql> CALL sys.ML_MODEL_UNLOAD('classification_use_case');

   To avoid consuming too much memory, it is good practice to unload a model when you are finished using it.

What's Next

• Review other Machine Learning Use Cases.