class sklearn.impute.MissingIndicator(*, missing_values=nan, features='missing-only', sparse='auto', error_on_new=True) [source]
 
Binary indicators for missing values. Note that this component typically should not be used in a vanilla Pipeline consisting of transformers and a classifier, but rather could be added using a FeatureUnion or ColumnTransformer. Read more in the User Guide.  New in version 0.20.   Parameters 
 
missing_valuesint, float, string, np.nan or None, default=np.nan 

The placeholder for the missing values. All occurrences of missing_values will be imputed. For pandas’ dataframes with nullable integer dtypes with missing values, missing_values should be set to np.nan, since pd.NA will be converted to np.nan.  
features{‘missing-only’, ‘all’}, default=’missing-only’ 

Whether the imputer mask should represent all or a subset of features.  If ‘missing-only’ (default), the imputer mask will only represent features containing missing values during fit time. If ‘all’, the imputer mask will represent all features.   
sparsebool or ‘auto’, default=’auto’ 

Whether the imputer mask format should be sparse or dense.  If ‘auto’ (default), the imputer mask will be of same type as input. If True, the imputer mask will be a sparse matrix. If False, the imputer mask will be a numpy array.   
error_on_newbool, default=True 

If True, transform will raise an error when there are features with missing values in transform that have no missing values in fit. This is applicable only when features='missing-only'.    Attributes 
 
features_ndarray, shape (n_missing_features,) or (n_features,) 

The features indices which will be returned when calling transform. They are computed during fit. For features='all', it is to range(n_features).     Examples >>> import numpy as np
>>> from sklearn.impute import MissingIndicator
>>> X1 = np.array([[np.nan, 1, 3],
...                [4, 0, np.nan],
...                [8, 1, 0]])
>>> X2 = np.array([[5, 1, np.nan],
...                [np.nan, 2, 3],
...                [2, 4, 0]])
>>> indicator = MissingIndicator()
>>> indicator.fit(X1)
MissingIndicator()
>>> X2_tr = indicator.transform(X2)
>>> X2_tr
array([[False,  True],
       [ True, False],
       [False, False]])
 Methods  
fit(X[, y]) Fit the transformer on X.  
fit_transform(X[, y]) Generate missing values indicator for X.  
get_params([deep]) Get parameters for this estimator.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Generate missing values indicator for X.    
fit(X, y=None) [source]
 
Fit the transformer on X.  Parameters 
 
X{array-like, sparse matrix}, shape (n_samples, n_features) 

Input data, where n_samples is the number of samples and n_features is the number of features.    Returns 
 
selfobject 

Returns self.     
  
fit_transform(X, y=None) [source]
 
Generate missing values indicator for X.  Parameters 
 
X{array-like, sparse matrix}, shape (n_samples, n_features) 

The input data to complete.    Returns 
 
Xt{ndarray or sparse matrix}, shape (n_samples, n_features) or (n_samples, n_features_with_missing) 

The missing indicator for input data. The data type of Xt will be boolean.     
  
get_params(deep=True) [source]
 
Get parameters for this estimator.  Parameters 
 
deepbool, default=True 

If True, will return the parameters for this estimator and contained subobjects that are estimators.    Returns 
 
paramsdict 

Parameter names mapped to their values.     
  
set_params(**params) [source]
 
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.  Parameters 
 
**paramsdict 

Estimator parameters.    Returns 
 
selfestimator instance 

Estimator instance.     
  
transform(X) [source]
 
Generate missing values indicator for X.  Parameters 
 
X{array-like, sparse matrix}, shape (n_samples, n_features) 

The input data to complete.    Returns 
 
Xt{ndarray or sparse matrix}, shape (n_samples, n_features) or (n_samples, n_features_with_missing) 

The missing indicator for input data. The data type of Xt will be boolean.