observationImputerComponent

Pipeline component for imputing missing values

Since R2026a

Description

observationImputerComponent is a pipeline component that imputes missing values in data observations. The pipeline component identifies and imputes missing values for a set of observations during the learn phase. During the run phase, the component uses the values learned during the learn phase to impute missing values in new observations.

Creation

Syntax

component = observationImputerComponent

component = observationImputerComponent(Name=Value)

Description

component = observationImputerComponent creates a pipeline component for imputing missing values.

component = observationImputerComponent(Name=Value) sets writable Properties using one or more name-value arguments. For example, you can specify the imputation method by using the Method name-value argument.

Properties

Learn Parameters

The software sets learn parameters when you create the component. You can modify learn parameters using dot notation any time before you use the learn object function. Any unset learn parameters use the corresponding default values.

Method — Imputation method

"mean" | "median" | "mode"

Imputation method, specified as "mean", "median", or "mode".

Value	Description
"mean"	For each variable, replace the missing values with the average (mean) value. This method is not supported for categorical variables.
"median"	For each variable, replace the missing values with the median value. This method is not supported for categorical variables.
"mode"	For each variable, replace the missing values with the value that appears most frequently in the variable.

If you do not specify the Method value, the software uses "median" for numeric variables and "mode" for categorical variables.

Example: c = observationImputerComponent(Method="mean")

Example: c.Method = "mode"

Data Types: char | string

Component Properties

The software sets component properties when you create the component. You can modify the component properties (excluding HasLearnables and HasLearned) using dot notation at any time. You cannot modify the HasLearnables and HasLearned properties directly.

Name — Component identifier

"ObservationImputer" (default) | character vector | string scalar

Component identifier, specified as a character vector or string scalar.

Example: c = observationImputerComponent(Name="Imputation")

Example: c.Name = "Impute"

Data Types: char | string

Inputs — Names of input ports

"DataIn" (default) | character vector | string array | cell array of character vectors

Names of the input ports, specified as a character vector, string array, or cell array of character vectors.

Example: c = observationImputerComponent(Inputs="X")

Example: c.Inputs = "X1"

Data Types: char | string | cell

Outputs — Names of output ports

"DataOut" (default) | character vector | string array | cell array of character vectors

Names of the output ports, specified as a character vector, string array, or cell array of character vectors.

Example: c = observationImputerComponent(Outputs="FilledX")

Example: c.Outputs = "FilledX1"

Data Types: char | string | cell

InputTags — Tags that enable automatic connection of component inputs

1 (default) | nonnegative integer vector

Tags that enable the automatic connection of the component inputs with other components or pipelines, specified as a nonnegative integer vector. If you specify InputTags, the number of tags must match the number of inputs in Inputs.

Example: c = observationImputerComponent(InputTags=0)

Example: c.InputTags = 1

Data Types: single | double

OutputTags — Tags that enable automatic connection of component outputs

1 (default) | nonnegative integer vector

Tags that enable the automatic connection of the component outputs with other components or pipelines, specified as a nonnegative integer vector. If you specify OutputTags, the number of tags must match the number of outputs in Outputs.

Example: c = observationImputerComponent(OutputTags=0)

Example: c.OutputTags = 1

Data Types: single | double

HasLearnables — Indicator for learnables

Read-only: 1 (true) (default)

This property is read-only.

Indicator for the learnables, returned as 1 (true). A value of 1 indicates that the component contains Learnables.

Data Types: logical

HasLearned — Indicator showing learning status of component

Read-only: 0 (false) (default) | 1 (true)

This property is read-only.

Indicator showing the learning status of the component, returned as 0 (false) or 1 (true). A value of 1 indicates that the learn object function has been applied to the component, and the Learnables are nonempty.

Data Types: logical

Learnables

The software sets learnables when you use the learn object function. You cannot modify learnables directly.

ImputedValues — Learned values to use for imputation

Read-only: cell array | []

This property is read-only.

Learned values to use for imputation, returned as a cell array. Each value corresponds to a variable in ImputedVariables.

ImputedVariables — Names of variables used by component

Read-only: string array | []

This property is read-only.

Names of the variables used by the component to derive ImputedValues, returned as a string array. The variables correspond to columns in the data argument of learn.

Data Types: string

Object Functions

learn	Initialize and evaluate pipeline or component
run	Execute pipeline or component for inference after learning
reset	Reset pipeline or component
series	Connect components in series to create pipeline
parallel	Connect components or pipelines in parallel to create pipeline
view	View diagram of pipeline inputs, outputs, components, and connections

Examples

Create Component for Imputing Missing Values

Create a pipeline component that imputes missing values in observations.

component = observationImputerComponent

component = 

  observationImputerComponent with properties:

                Name: "ObservationImputer"
              Inputs: "DataIn"
           InputTags: 1
             Outputs: "DataOut"
          OutputTags: 1

   
Learnables (HasLearned = false)
    ImputedVariables: []
       ImputedValues: []


Show all parameters

component is an observationImputerComponent object that contains two learnables: ImputedVariables and ImputedValues. The properties remain empty until you pass data to the component during the learn phase.

Load the carbig data set. Create a table containing the variables Acceleration, Displacement, and Horsepower.

load carbig
cars = table(Acceleration,Displacement,Horsepower);

Note that the Horsepower variable contains missing values. For example, display the 338th observation in cars.

observation = cars(338,:)

observation =

  1×3 table

    Acceleration    Displacement    Horsepower
    ____________    ____________    __________

        17.3             85            NaN    

Use the learn object function to find and impute missing values in cars.

[component,newcars] = learn(component,cars);
component

component = 

  observationImputerComponent with properties:

                Name: "ObservationImputer"
              Inputs: "DataIn"
           InputTags: 1
             Outputs: "DataOut"
          OutputTags: 1

   
Learnables (HasLearned = true)
    ImputedVariables: ["Acceleration"    "Displacement"    "Horsepower"]
       ImputedValues: {[15.5000]  [151]  [95]}


Show all parameters

The ImputedVariables and ImputedValues properties are nonempty, and the HasLearned property is set to true.

Note that the 338th observation in newcars uses the imputed Horsepower value of 95.

newObservation = newcars(338,:)

newObservation =

  1×3 table

    Acceleration    Displacement    Horsepower
    ____________    ____________    __________

        17.3             85             95    

Version History

Introduced in R2026a