RapidMiner Performance Regression in Kuwait operator is used for statistical performance evaluation of regression tasks and delivers a list of performance criteria values of the regression task.
This operator must be utilized for performance evaluation of the regression tasks only. The Performance Regression operator is used with regression tasks only.
On the other hand, the Performance operator automatically determines the learning task type and calculates the most common criteria for that type. You can use the Performance (User-Based) operator if you want to write your own performance measure.
RapidMiner Performance Regression in Kuwait Input and Output
- Labeled data
This input port expects a labeled ExampleSet. The Apply Model operator is an example of such operators that give labeled data. Make sure that the ExampleSet has the label and prediction attribute. See the Set Role operator for more details regarding the label and prediction roles of attributes.
This is an optional parameter. It needs a Performance Vector.
This port provides a Performance Vector. Performance Vector is a record of performance criteria values. It is calculated on the basis of the label and prediction attribute of the input ExampleSet.
The output performance vector consists of performance criteria calculated by this Performance operator. If a Performance Vector was also fed at the performance input port, the criteria of the input-performance-vector are also added in the output-performance-vector.
If the input-performance-vector and the calculated-performance-vector both have the same criteria but with different values, the values of the calculated-performance-vector are delivered through the output port.
- ExampleSet (IOObject)
The ExampleSet that was provided as input is sent without changing to the output through this port. This is usually used to reuse the same ExampleSet in further operators or to view the ExampleSet in the Results Workspace.
Regression is a technique used for numerical prediction and it is a statistical measure that attempts to determine the strength of the relationship between one dependent variable ( i.e. the label attribute) and a series of other changing variables known as independent variables (regular attributes).
Just like Classification is used for predicting categorical labels, Regression is used for predicting a continuous value. Regression is often utilized to determine the particular factors such as the price of interest rates, commodity, particular industries or sectors influence the price movement of an asset.
For evaluating the statistical performance of a regression model the data set should be labeled i.e. it should have an attribute with label role and an attribute with prediction role.
The label attribute stores the actual observed values whereas the prediction attribute stores the values of label predicted by the regression model under discussion. For more details on RapidMiner Performance Regression in Kuwait, please contact us.