Imputer

Replaces missing values in a dataset with a computed value infered from the remained of the dataset.

Configuration:

• missing_values

  The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value “NaN”.

• strategy

  The imputation strategy.

  • If “mean”, then replace missing values using the mean along the axis.
  • If “median”, then replace missing values using the median along the axis.
  • If “most_frequent”, then replace missing using the most frequent value along the axis.

• axis

  The axis along which to impute.

  • If axis=0, then impute along columns.
  • If axis=1, then impute along rows.

Attributes:

• statistics_

  The imputation fill value for each feature if axis == 0.

Input ports:

Output ports:

model : model

Model

missing_values (missing_values)

The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value “NaN”.

strategy (strategy)

The imputation strategy.

• If “mean”, then replace missing values using the mean along the axis.
• If “median”, then replace missing values using the median along the axis.
• If “most_frequent”, then replace missing using the most frequent value along the axis.

axis (axis)

The axis along which to impute.

• If axis=0, then impute along columns.
• If axis=1, then impute along rows.

Some of the docstrings for this module have been automatically extracted from the scikit-learn library and are covered by their respective licenses.

class node_preprocessing.Imputer