Я привык строить диаграммы в Statistica. Там сразу же можно было задавать функции, аргументы и параметры. А здесь поиск привел меня к новому понятию - "Роль". Получается, что надо сначала переназначить роль метки (Label), а потом строить диаграмму с параметрами. Set Role operator should not be confused with Rename operator or Type Conversion operators
Set Role operator. Role of an attribute reflects the part played by that attribute in an ExampleSet.¶
Changing the role of an attribute may change the part played by that attribute in a process. One attribute can have exactly one role. This operator is used to change the role of one or more attributes of the input ExampleSet. This is
a very simple operator, all you have to do is to select an attribute and select a new role for it. Different learning operators require attributes with different roles.
This operator is frequently used to set the right roles for attributes before applying the desired operator. The change in role is only for the current process, i.e. Role of attribute is not changed permanently in the ExampleSet. Set Role operator should not be confused with Rename operator or Type Conversion operators.
Rename operator is used to change the name of an attribute. Many type Conversion operators are available (at Data Transformation/Type conversion/) to change the type of attributes e.g. Nominal to Binominal operator, Numerical
to Polynomial operator and many more.
Broadly roles are classified into two types i.e. regular and special. Regular attributes simply describe the examples. Regular attributes are usually used during learning processes. One ExampleSet can have numerous regular attributes.
Special attributes are those which identify the examples separately. Special attributes have some specific task. Special roles are: label, id, prediction, cluster, weight, and batch.
An ExampleSet can have numerous special attributes but one special role cannot be repeated. If one special role is assigned to more than one attributes in an ExampleSet, all such attributes will be dropped except the last one. This concept can be understood easily by studying the attached Example Process. Explanation of various roles is given in the parameters section.
regular Attributes without a special role, i.e. those which simply describe the examples are called regular attributes and just leave out the role designation in most cases. Regular attributes are used as input variables for learning tasks
id This is a special role, it acts as id attribute for the ExampleSet and it is usually unique in every example of the ExampleSet. Id role is used to clearly identify the examples of concerned ExampleSet. In this case the attribute adopts the role of an identifier and is called ID for short. Unique ids can be given to all the examples using Generate ID operator.
label This is a special role, it acts as a target attribute for learning operators e.g. Decision Tree operator. Labels identify the examples in any way and they must be predicted for new examples that are not yet characterized in such a manner. Label is also called 'goal variable'.
prediction This is a special role, it acts as predicted attribute of a learning scheme. For example when a predictive model is learnt through any learning operator and then it is applied using Apply Model operator, in the output we have a new attribute with role prediction which holds the values of label predicted by the given model. Label and prediction attributes are also used for evaluating performance of a model.
cluster This is a special role, it indicates the membership of an example of the ExampleSet to a particular cluster. For example, output of k-Mean operator adds a column with cluster role.
weight This is a special role, it indicates the weight of the examples with regard to the label. Weights are used in learning processes to give different importance to examples with different weights.
Attribute weights are used in numerous operators e.g. Select By Weights operator. Weights can also be used in evaluating performance of models e.g. Performance operator has use example weights parameter to consider weight of examples during performance evaluation process.
batch This is a special role, it indicates the membership to an example batch.
user defined Any role can be provided by directly typing in the textbox instead of selecting a role from the dropdown menu. If 'ignore' is written in the textbox, that attribute will be ignored by the coming operators in the process. This is also a special role, thus it needs to be unique. To ignore multiple attributes unique roles can be assigned like ignore01, ignore02, igonre03 and so on.
set additional roles (menu) Click this button to modify roles of more than one attributes. A click on this button opens a new menu which allows you to select any attribute and assign any role to it. It also allows assigning multiple roles to the same attribute. But, as an attribute can have exactly one role, only the last role assigned to that attribute is actually assigned to it and all previous roles assigned to it are ignored.
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