|  | ## Impute Missing Values

#### Synopsis

Replaces missing values in examples by applying a model learned for missing values.

#### Description

The operator MissingValueImpution imputes missing values by learning models for each attribute (except the label) and applying those models to the data set. The learner which is to be applied has to be given as inner operator. In order to specify a subset of the example set in which the missing values should be imputed (e.g. to limit the imputation to only numerical attributes) the corresponding attributes might be chosen by the filter parameters. Please be aware that depending on the ability of the inner operator 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 MissingValueReplenishment. ATTENTION: This operator is currently under development and does not properly work in all cases. We do not recommend the usage of this operator in production systems.

#### Input

- example set in: expects: ExampleSet

#### Output

- example set out:

#### Parameters

- attribute filter type: The condition specifies which attributes are selected or affected by this operator.
- attribute: The attribute which should be chosen.
- attributes: The attribute which should be chosen.
- regular expression: A regular expression for the names of the attributes which should be kept.
- use except expression: If enabled, an exception to the specified regular expression might be specified. Attributes of matching this will be filtered out, although matching the first expression.
- except regular expression: A regular expression for the names of the attributes which should be filtered out although matching the above regular expression.
- value type: The value type of the attributes.
- use value type exception: If enabled, an exception to the specified value type might be specified. Attributes of this type will be filtered out, although matching the first specified type.
- except value type: Except this value type.
- block type: The block type of the attributes.
- use block type exception: If enabled, an exception to the specified block type might be specified.
- except block type: Except this block type.
- numeric condition: Parameter string for the condition, e.g. '>= 5'
- invert selection: Indicates if only attributes should be accepted which would normally filtered.
- include special attributes: Indicate if this operator should also be applied on the special attributes. Otherwise they are always kept.
- iterate: Impute missing values immediately after having learned the corresponding concept and iterate.
- learn on complete cases: Learn concepts to impute missing values only on the basis of complete cases (should be used in case learning approach can not handle missing values).
- order: Order of attributes in which missing values are estimated.
- sort: Sort direction which is used in order strategy.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess