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Data Pilot - Importance for classification
 

The method allows comparing attributes based on their importance for classification without a learning sample – performing cluster analysis.

The attributes can be measured by the naming scale (start with “n:”), order scale (“o:”), or interval scale (start with “i:” or do not have “:”). Different attribute values are defined by the whole numbers.

The samples may have different sizes, but in order to compare the significance of attributes between each other, small samples must have the same size. Larger samples (over 10 observations) may vary in their sizes.

The values for the importance for classification may vary from 0 (no correlation) to 1 (proven correlation). The results are on the order scale, meaning we can state that one attribute is more valuable than the other, but can not say by how much.

Example

After indicating the basic data area and the results area, select from the dropout menu “Confidence Interval” and press the "Next >>" button.

This method does not require setting any parameters.

The "<< Back" button allows users to come back to the original data and calculation method selection.

Press the “Calc” button to start calculations.

During the process of calculation, a progress window appears. The “Cancel” button allows user to stop the calculations. After the calculation is complete, you get the results.

 

Examples:
Confidence Interval
Comparing Averages
Correlation Significance
Classification Importance
Send us your own example
 
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