Impute

Replaces unknown values in the data.

Channels

Inputs

Examples (ExampleTable)
Data set.
Learner for Imputation
A learning algorithm to be used when values are imputed using a predictive model. This algorithm, if given, substitutes the default (1-NNLearner).

Outputs

Examples (ExampleTable)
The same data set as on the input, but with the missing values imputed.

Description

Some Orange's algorithms and visualization cannot handle unknown values in the data. This widget does what statistician call imputation: it substitutes them by values computed from the data or set by the user.

Impute widget

In the top-most box, Default imputation method, the user can specify a general imputation technique for all attributes.

It is also possible to specify individual treatment for each attribute which override the default treatment set above. One can also specify a manually defined value used for imputation. In the snapshot on the left, we decided not to impute the values of "normalized-losses" and "make", the missing values of "aspiration" will be replaced by random values, while the missing values of "body-style" and "drive-wheels" are replaced by "hatchback" and "fwd", respectively. If the values of "length", "width" or "height" is missing, the example is discarded. Values of all other attributes use the default method set above (model-based imputer, in our case).

Button Set All to Default resets the individual attribute treatments to the default.

Imputing class values is typically not a good practice, so it is off by default. It can be enabled by checking Impute class values. If checked and the default method is to remove the examples with missing values, then also examples with unknown classes are removed; otherwise they are not.

All changes are committed immediately is Send automatically is checked. Otherwise, Apply needs to be pushed to apply any new settings.