In the second dialog box of k-NN, select Normalize input data, type 5
for Number of nearest neighbors. .
Normalize
Input data: Check
this option if the input data are to be normalized (this will express
all data in terms of standard deviations so that the distance measure is
not dominated by variables with a large scale).
Number of
Nearest Neighbors: This is the parameter k in the k-nearest neighbor
algorithm. The value of k should be between 1 and the total number of
observations (rows). Typically, this is chosen to be in units or
tens.
Scoring option : Select the
second option for this example. Thus XLMiner™ will display the output for the
best K between 1 and 5. If we select the first option, the output will be
displayed for the specified value of k.
Score training data:
Select this option to show an assessment of the performance in
predicting the training data. The report is displayed according to
your specifications - Detailed, Summary and Lift charts.
Score validation data:
Select this option to show an assessment of the performance in
predicting the validation data. The report is displayed according to
your specifications - Detailed, Summary and Lift charts.
Score Test Data: The options in this group let you apply the model for scoring to the test partition (if one had been created
earlier). The option "Score Test Data" is available only if the dataset contains test partition. Select it to apply the model to test data.
Score new Data: The options in this group let you apply the model for scoring to an altogether new data.
Specify where the new data is located. See
the Example of Discriminant Analysis for detailed instructions on this.
Score New data in database : See
the Example of Discriminant Analysis for detailed instructions on this.
click on Finish
