This document introduces data partitioning in XLMiner. Partitioning divides a large dataset into training, validation, and test sets. The training set builds a model, the validation set checks the model's accuracy, and the test set determines real-world performance. XLMiner allows standard partitioning with random assignment or specifying percentages, as well as oversampling to balance low-success outputs.

1. Introduction toXLMiner™: PARTITION DATAXLMiner and Microsoft Office are registered trademarks of the respective owners.

2. Introduction to Partition DataGenerally the data sets used in mining are enormous. Hence in order to mine data easily ,one method is to divide/partition data. Partitioning data means dividing the data set into multiple partitions that are mutually exclusive i.e. they do not overlap or the partitions have no data records are common.Partitioning data generally results in 3 sets of data:Training Data set :- This partition is used to create/build the mining model.Validation Data set :- : It is used to check whether the model developed using the training set is accurate or not. The validation set consists of data whose result (the value of the variable to be determined) is already known so that results obtained after applying the model and the actual results can be matched.Test data set :- It is used to determine how the model would perform when it encounters real world data. http://dataminingtools.net

3. Types of PartitionsXLMiner allows us to create 2 kinds of partitions:Standard Partition: Creates 3 partitions based on the partition ratios provided. Data records are randomly elected and every record has an equal chance of lying in any of the partition.Automatic: When this is selected, the wizard by default sets the partitioning ratio as 60(training):40(validation) and no test set is created. These values cannot be altered.

4. Specify percentages :Unlike automatic, if selected ,the user can specify the ratio of the partitions created in terms of percentages.

5. Equal partitions: Selecting this option sets a partitioning ratio of 33.3(training): 33.3(validation): 33.3(test) .Partition with oversampling: This method of partitioning is used when the percentage of successes in the output variable is very low in the dataset but we want to train the data with a particular percentage of successes.http://dataminingtools.net

6. Data Set used for Partitionhttp://dataminingtools.net

7. Standard Partition (Automatic)-Step 1http://dataminingtools.net

8. Standard Partition (Automatic)-Output Testing Set Validation Sethttp://dataminingtools.net

9. Standard Partition (Specify)-Step 1Selecting “Specify percentages” allows us to set the partitioning ratios as per our need. Here we have set a ratio of 50(testing):30(validation):20(test)http://dataminingtools.net

10. Standard Partition (Equal)-Step 1Selecting “Equal” sets the partitioning ratio at 33.3% for each partition creating 3 equal sized partitions.http://dataminingtools.net

11. Oversampled Partition – Data SetIn order to oversample a data set, it must contain at least 1 data item that accepts only 2 distinct values, not more and only then can it be used as the success class(the data item which is oversampled)http://dataminingtools.net

12. Oversampled Partition – Step 1http://dataminingtools.net

13. Oversampled Partition – OutputThe records in the training data sethttp://dataminingtools.net

14. Oversampled Partition – OutputRows in Validation set = 27, Rows in testing set = 30% of 27 = 12.http://dataminingtools.net