Name

Description

Number of training patterns

Indicate the number of training patterns, either as an absolute number of patterns or as a percentage of the overall dataset, which will be used to create the training set.

These patterns are used to build the model.

Starting pattern for training set

Indicate the starting point for the training set pattern either as an absolute value, or as a percentage of the whole.

This option is valid only if No Shuffle is selected as the Data reshuffle policy.

Number of test patterns

Indicate the number of test patterns, either as an absolute number of patterns or as a percentage of the overall dataset,  which will be used to create the test set.

These patterns are not used to build the model.

Starting pattern for test set

Indicate the starting point for the test set pattern either as an absolute value, or as a percentage of the whole.

This option is valid only if No Shuffle is selected as the Data reshuffle policy.

Number of validation patterns

Indicate the number of validation patterns, either as an absolute number of patterns or as a percentage of the overall dataset, which will be used to create the validation set.

The validation patterns cannot be used to create or test the model and are not mandatory as they are are used only for internal validation by some modeling methods.

Starting pattern for validation set

Indicate the starting point for the validation set pattern either as an absolute value, or as a percentage of the whole.

This option is valid only if No Shuffle is selected as the Data reshuffle policy.

Data reshuffle policy

Indicate the required Data shuffle policy from the drop-down list:

• No shuffle, if you want to use the defined starting points only to define the training, test and validation patterns. Any previous shuffles are removed and the natural pattern order is used.

• Reshuffle, if you want the order of the patterns to be randomly shuffled before applying the starting pattern values to the respective set. The shuffle procedure is only used to create the three subsets and it does not affect the real data order. A random choice usually ensures better results by improving the statistical representation of the training set. The resulting analysis cannot be identically reproduced as different Split Data tasks can produce dissimilar training sets, unless a seed is set for initialization.

• Keep shuffle, if you want to preserve any previous shuffles.

Initialize random generator with seed

Select Initialize random generator with seed if you want to set the seed for the random generator. This may be useful to make each execution reproducible. Otherwise, each execution of the same task (with same options) may produce dissimilar results due to the different random numbers generated to define training/test/validation sets.

• After having imported the dataset, right-click on the import task and click Take a look to check the data have been correctly imported.

• The imported dataset contains 32561 patterns.

• Add a Split Data task and link it to the import task. Double click the task to open it.

• We want to divide the dataset in two parts:

  • 70% of data will be the training set;

  • 30% of data will be the test set.

  • No validation test is needed.

• Save and compute the task.

Add a Data Manager onto the stage and link it to the split data.

In the Modeling Sets section, click on the Training set and the Test set to display the divided data. We will have:

• 16281 patterns in the training set.

• 16280 patterns in the test set.