(AI Studio Core)

Synopsis

This operator converts the selected numerical attributes into nominal attributes by discretizing the numerical attribute into a user-specified number of bins. Bins of equal frequency are automatically generated, the range of different bins may vary.

Description

This operator discretizes the selected numerical attributes to nominal attributes. The number of bins parameter is used to specify the required number of bins. The number of bins can also be specified by using the use sqrt of examples parameter. If the use sqrt of examples parameter is set to true, then the number of bins is calculated as the square root of the number of examples with non-missing values (calculated for every single attribute). This discretization is performed by equal frequency binning i.e. the thresholds of all bins is selected in a way that all bins contain the same number of numerical values. Numerical values are assigned to the bin representing the range segment covering the numerical value. Each range is named automatically. The naming format for the range can be changed using the range name type parameter. Values falling in the range of a bin are named according to the name of that range.

Other discretization operators are also available. The Discretize by Frequency operator creates bins in such a way that the number of unique values in all bins are (almost) equal. In contrast, the Discretize by Binning operator creates bins in such a way that the range of all bins is (almost) equal.

Differentiation

Discretize by Binning

The Unknown operator creates bins in such a way that the range of all bins is (almost) equal.

Discretize by Size

The Discretize by Size operator creates bins in such a way that each bin has user-specified size (i.e. number of examples).

Discretize by Entropy

The discretization is performed by selecting bin boundaries such that the entropy is minimized in the induced partitions.

Discretize by User Specification

This operator discretizes the selected numerical attributes into user-specified classes.

Input

example set (Data table)
This input port expects an ExampleSet. It is the output of the Retrieve operator in attached Example Process. The output of other operators can also be used as input. Please note that there should be at least one numerical attribute in the input ExampleSet, otherwise use of this operator does not make sense.

Output

example set (Data table)
The selected numerical attributes are converted into nominal attributes by discretization (frequency) and the resultant ExampleSet is delivered through this port.
original (Data table)
The ExampleSet that was given as input is passed 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.
preprocessing model (Preprocessing Model)
This port delivers the preprocessing model, which has information regarding the parameters of this operator in the current process.

Parameters

attribute filter typeThis parameter allows you to select the attribute selection filter; the method you want to use for selecting attributes. It has the following options:
- all: This option simply selects all the attributes of the ExampleSet. This is the default option.
- single: This option allows selection of a single attribute. When this option is selected another parameter (attribute) becomes visible in the Parameters panel.
- subset: This option allows selection of multiple attributes through a list. All attributes of ExampleSet are present in the list; required attributes can be easily selected. This option will not work if meta data is not known. When this option is selected another parameter becomes visible in the Parameters panel.
- regular expression: This option allows you to specify a regular expression for attribute selection. When this option is selected some other parameters (regular expression, use except expression) become visible in the Parameters panel.
- value type: This option allows selection of all the attributes of a particular type. It should be noted that types are hierarchical. For example real and integer types both belong to the numeric type. Users should have basic understanding of type hierarchy when selecting attributes through this option. When this option is selected some other parameters (value type, use value type exception) become visible in the Parameters panel.
- block type: This option is similar in working to the value_type option. This option allows selection of all the attributes of a particular block type. It should be noted that block types may be hierarchical. For example value_series_start and value_series_end block types both belong to the value_series block type. When this option is selected some other parameters (block type, use block type exception) become visible in the Parameters panel.
- no missing values: This option simply selects all the attributes of the ExampleSet which don't contain a missing value in any example. Attributes that have even a single missing value are removed.
- numeric value filter: When this option is selected another parameter (numeric condition) becomes visible in the Parameters panel. All numeric attributes whose examples all satisfy the mentioned numeric condition are selected. Please note that all nominal attributes are also selected irrespective of the given numerical condition.
attributeThe required attribute can be selected from this option. The attribute name can be selected from the drop down box of the parameter attribute if the meta data is known.
attributesThe required attributes can be selected from this option. This opens a new window with two lists. All attributes are present in the left list and can be shifted to the right list, which is the list of selected attributes.
regular expressionThe attributes whose name match this expression will be selected. Regular expression is a very powerful tool but needs a detailed explanation to beginners. It is always good to specify the regular expression through the edit and preview regular expression menu. This menu gives a good idea of regular expressions and it also allows you to try different expressions and preview the results simultaneously.
use except expressionIf enabled, an exception to the first regular expression can be specified. When this option is selected another parameter (except regular expression) becomes visible in the Parameters panel.
except regular expressionThis option allows you to specify a regular expression. Attributes matching this expression will be filtered out even if they match the first regular expression (regular expression that was specified in the regular expression parameter).
value typeThe type of attributes to be selected can be chosen from a drop down list.
use value type exception If enabled, an exception to the selected type can be specified. When this option is enabled, another parameter (except value type) becomes visible in the Parameters panel.
except value typeThe attributes matching this type will not be selected even if they match the previously mentioned type i.e. value type parameter's value.
block typeThe block type of attributes to be selected can be chosen from a drop down list.
use block type exceptionIf enabled, an exception to the selected block type can be specified. When this option is selected another parameter (except block type) becomes visible in the Parameters panel.
except block typeThe attributes matching this block type will be not be selected even if they match the previously mentioned block type i.e. block type parameter's value.
numeric conditionThe numeric condition for testing examples of numeric attributes is specified here. For example the numeric condition '> 6' will keep all nominal attributes and all numeric attributes having a value of greater than 6 in every example. A combination of conditions is possible: '> 6 && < 11' or '<= 5 || < 0'. But && and || cannot be used together in one numeric condition. Conditions like '(> 0 && < 2) || (>10 && < 12)' are not allowed because they use both && and ||. Use a blank space after '>', '=' and '<' e.g. '<5' will not work, so use '< 5' instead.
include special attributesThe special attributes are attributes with special roles which identify the examples. In contrast regular attributes simply describe the examples. Special attributes are: id, label, prediction, cluster, weight and batch. By default all special attributes are selected irrespective of the conditions in the Select Attributes operator. If this parameter is set to true, Special attributes are also tested against conditions specified in the Select Attributes operator and only those attributes are selected that satisfy the conditions.
invert selectionIf this parameter is set to true, it acts as a NOT gate, it reverses the selection. In that case all the selected attributes are unselected and previously unselected attributes are selected. For example if attribute 'att1' is selected and attribute 'att2' is unselected prior to checking of this parameter. After checking of this parameter 'att1' will be unselected and 'att2' will be selected.
use sqrt of examplesIf set to true, the number of bins is determined by the square root of the number of non-missing values instead of using the number of bins parameter.
number of binsThis parameter is available only when the use sqrt of examples parameter is not set to true. This parameter specifies the number of bins which should be used for each attribute.
range name typeThis parameter is used for changing the naming format for range. 'long', 'short' and 'interval' formats are available.
automatic number of digitsThis is an expert parameter. It is only available when the range name type parameter is set to 'interval'. It indicates if the number of digits should be automatically determined for the range names.
number of digitsThis is an expert parameter. It is used to specify the minimum number of digits used for the interval names.

Tutorial Processes

Discretizing the Temperature attribute of the 'Golf' data set by Frequency

The focus of this Example Process is the discretization (by frequency) procedure. For understanding the parameters related to attribute selection please study the Example Process of the Select Attributes operator.

The 'Golf' data set is loaded using the Retrieve operator. The Discretize by Frequency operator is applied on it. The 'Temperature' attribute is selected for discretization. The number of bins parameter is set to 3. Run the process and switch to the Results Workspace. You can see that the 'Temperature' attribute has been changed from numerical to nominal form. The values of the 'Temperature' attribute have been divided into three ranges. Each range has an equal number of unique values. You can see that 'range1' and 'range3' have 4 examples while the 'range2' has 6 examples. But in 'range2' the 'Temperature' values 72 and 75 occur twice. Thus essentially 4 unique numerical values are present in 'range2'.

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