Java Reference
In-Depth Information
Test Metrics for Model Evaluation
This section introduces interfaces and methods used to compute
and retrieve classification test metrics using JDM. Table 9-14 lists
the classification test metrics-related interfaces. JDM provides two
types of tasks for computing test metrics for supervised functions:
supervised.TestTask and supervised.TestMetricsTask . The TestTask
requires a supervised model and test data, whereas the TestMetric-
sTask interface uses apply output data that includes actual and pre-
dicted target values. In this example, we use ClassificationTestTask
and illustrate how the TestMetricsTask interface is used for the
regression example shown later in Section 9.5.
Listing 9-11 shows the code that extends the CustomerAttrition
class with the attrition_model evaluation. Recall that section 7.1.6
illustrates classification test metrics such as accuracy, error, confu-
sion matrix, lift, and receiver operating characteristics (ROC). Listing
9-11 shows the computation and retrieval of the classification test
metrics. Lines 15 to 22 show the creation and execution of the attri-
tionTestTask that computes the test metrics of the attrition_model
using the CUSTOMERS_TEST_DATA. Lines 27 to 29 show the
retrieval of the attrition_test_metrics object that was created by the test
Table 9-14
Classification test metrics-related interfaces
javax.datamining.supervised.classification package
A ClassificationTestTask is used for testing a classification model to
measure the model quality on test data.
A ClassificationTestMetrics encapsulates classification test metrics
such as confusion matrix, lift, and ROC. It provides get methods to
retrieve these metrics.
A ConfusionMatrix specifies the statistics of the correct predictions
and errors.
A Lift specifies the results of the lift computation. It contains the
lift, target denisity details for each quantile. Using this object, one
can plot the lift charts that are described in Chapter 7.
A ReceiverOperatingCharacteristics specifies the result of receiver
operating characteristic computation. It contains the false and true
positive rates at various probability thresholds. Using this object,
one can plot the ROC charts described in Chapter 7.
A ClassificationTestMetricsTask is a mining task used to compute
test metrics given an apply output data.
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