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Don Burleson Blog 









Using SVM for Linear Values

Data warehouse tips by Burleson Consulting

This is an excerpt from Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse".

The SVM algorithm is a useful method for predicting the value of a continuous value.  To Build the regression model, choose Build from the Activity tool, and pick Regression as the function type.  Note that Support Vector Machineis the only algorithm available for regression. 

We will continue with the wizard as previously, choosing OBS as the unique identifier and NOXas the target attribute.  Under Advanced Settings, the tabs are the same as for the other SVM algorithms with the exception of the Build settings.  SVM will select and optimize all parameters, such as kernel function, tolerance etc, so we?ll keep the default settings and go ahead and build the model. 

Building the New SVM Model

The Build results show that SVM chose the Gaussiankernel for the algorithm, and the predictive confidence of the resulting model is between good and best at 66%.  There are several new measures available in the results of the regression model that indicate the ?goodness of fit? of the model. 

A good fit explains a high proportion of variability in the data, and is able to predict new cases with high certainty. 

ODMr  provides both graphical and statistical estimates of goodness of fit, a graphic plot of residuals and calculation of root mean square error.  Note that there is a residual plot available in the Build Activity

Residuals are the differences between the actual and predicted values.  If the residuals are randomly distributed around zero, then the model is a good fit.  Click on Result in the Residual Plot box to see the graphic. 

Dots on the red zero line means that the value was an exact prediction, whereas dots above and below the line show the relative error of the prediction.  You can see that the dots are randomly scattered until around NOX = 0.55, where the error of the predictions begin to vary considerably.   This indicates that the model is much more accurate for lower values of nitric oxide concentrations than for higher concentrations.  You can mouse over a data point to see the actual and predicted values.  For point 150 for example, the actual value (x axis) was 0.614 and the model predicted 0.7018.  If you were building regression models for air pollution, you might want to build one model for lower levels of NOX and another one for levels exceeding 0.5. 

Linear Regression Analysis

Checking the predicted circle at the bottom right of the residual plot will toggle between the actual and predicted plots.  The graph shows the predicted values on the x-axis and shows which predictions can be trusted the most.  As in the actual residual plot, the graph indicates that predicted values over 0.5 are inaccurate.  A predicted value of 0.7 may be very close to 0.7, or it could be 0.8 or 0.6. 

However, predicted values of 0.6 or less will be very close to 0.6.   Clicking on residual plot data will show a listing of the actual and predicted values for the test dataset. 

The statistical measures of goodness of fit are found under the Test Metric Result.  Here we have the Root Mean Square Error (RMSE) also known as the standard error of the regression.  An RMSE closer to zero means that the model is a better predictor.

Compare this result to that of using TAX as the target for a regression model.  Here we see that the majority of points are tightly clustered around the red zero line, the RMSEis 0.0418, and the predictive confidence is very good at 87%. 

This is a very highly accurate model for predicting tax rates for properties in the Boston housing dataset.


For more tips and tricks for Oracle data warehouse analysis, see Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse"

You can buy it direct from the publisher for 30%-off:


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