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Evaluate Model Quality: Compute Regression Test Metrics
In regression models, quality is measured by computing the cumulative
errors when comparing predicted values against known values. Gen-
erally, the lower the cumulative error the better the model perfor-
mance. There are many mathematical metrics that can be used to
quantify error, such as root mean squared (RMS) error, mean absolute
error , and R-squared error. Table 7-8 illustrates the computation of
some of these metrics by taking three cases. To compute the mean
absolute error, we take the ratio between the sum of the absolute
value of prediction errors and number of predictions. To compute the
root mean squared error, we first compute the mean of the prediction
error squares, then take the square root.
There is another metric called the R-squared value, which mea-
sures the relative predictive power of a model. R-squared is a
descriptive measure between 0 and 1. The closer it is to 1, the greater
the accuracy of the regression model. When R squared equals 1, the
regression makes perfect predictions. For more details about these
regression model evaluation metrics refer to [Witten/Frank 2005].
Apply Model: Obtain Prediction Results
After finding a regression model with minimum error, we apply that
model to new data to make predictions. The model signature, as
discussed for classification in Section 7.2.1, is also applicable for
regression. The apply data must provide all the attributes in the
model's signature. Of course, attribute values for some cases may be
missing and most models can function normally in the presence of
some missing values. The regression apply operation can produce
contents such as predicted value (model predicted target value) and
Table 7-8
Regression test metrics
Prediction error
(predicted (p)
Promotion ID
Predicted value
Actual value
actual (a))
Mean Absolute Error (50,000 20,000 70,000)/3
50,000 2
20,000 2
70,000 2 /3
Root Mean Squared Error
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