This operator estimates values for the missing values of the selected attributes by applying a model learned for missing values.
This is a nested operator i.e. it has a subprocess. This subprocess should always accept an ExampleSet and return a model. The Impute Missing Values operator estimates values for missing values by learning models for each attribute (except the label) and applying those models to the ExampleSet. The learner for estimating missing values should be placed in the subprocess of this operator. Please note that depending on the ability of the inner learner to handle missing values this operator might not be able to impute all missing values in some cases. This behavior leads to a warning. It might hence be useful to combine this operator with a subsequent Replace Missing Values operator.
The missing values in the ExampleSet are replaced by the values estimated by the given model and the resultant ExampleSet is output of this port.
single: This option allows selection of a single attribute. When this option is selected another parameter (attribute) becomes visible in 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 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 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 numeric type. User 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 Parameters panel.
block_type: This option is similar in working to 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 value_series block type. When this option is selected some other parameters (block type, use block type exception) become visible in Parameters panel.
numeric_value_filter: When this option is selected another parameter (numeric condition) becomes visible in Parameters panel. All numeric attributes whose all examples satisfy the mentioned numeric condition are selected. Please note that all nominal attributes are also selected irrespective of the given numerical condition.
The 'Labor-Negotiations' data set is loaded using the Retrieve operator. A breakpoint is inserted here so that you can view the ExampleSet. You can see that there are numerous missing values in this ExampleSet. The Impute Missing Values operator is applied on this ExampleSet for estimating missing values. Have a look at the subprocess of this operator. The K-NN operator is applied there for estimating the missing values. The attribute filter type parameter is set to 'all', thus missing values of all attributes will be estimated using the K-NN scheme. All parameters are used with default values. The resultant ExampleSet can be seen in the Results Workspace. You can see that there are no missing values in this ExampleSet because they have been estimated using the K-NN scheme.