sklearn.preprocessing.KBinsDiscretizer  
class sklearn.preprocessing.KBinsDiscretizer(n_bins=5, *, encode='onehot', strategy='quantile', dtype=None) [source]
 
Bin continuous data into intervals. Read more in the User Guide.  New in version 0.20.   Parameters 
 
n_binsint or array-like of shape (n_features,), default=5 

The number of bins to produce. Raises ValueError if n_bins < 2.  
encode{‘onehot’, ‘onehot-dense’, ‘ordinal’}, default=’onehot’ 

Method used to encode the transformed result.  onehot

Encode the transformed result with one-hot encoding and return a sparse matrix. Ignored features are always stacked to the right.  onehot-dense

Encode the transformed result with one-hot encoding and return a dense array. Ignored features are always stacked to the right.  ordinal

Return the bin identifier encoded as an integer value.    
strategy{‘uniform’, ‘quantile’, ‘kmeans’}, default=’quantile’ 

Strategy used to define the widths of the bins.  uniform

All bins in each feature have identical widths.  quantile

All bins in each feature have the same number of points.  kmeans

Values in each bin have the same nearest center of a 1D k-means cluster.    
dtype{np.float32, np.float64}, default=None 

The desired data-type for the output. If None, output dtype is consistent with input dtype. Only np.float32 and np.float64 are supported.  New in version 0.24.     Attributes 
 
n_bins_ndarray of shape (n_features,), dtype=np.int_ 

Number of bins per feature. Bins whose width are too small (i.e., <= 1e-8) are removed with a warning.  
bin_edges_ndarray of ndarray of shape (n_features,) 

The edges of each bin. Contain arrays of varying shapes (n_bins_, ) Ignored features will have empty arrays.      See also  
Binarizer


Class used to bin values as 0 or 1 based on a parameter threshold.    Notes In bin edges for feature i, the first and last values are used only for inverse_transform. During transform, bin edges are extended to: np.concatenate([-np.inf, bin_edges_[i][1:-1], np.inf])
 You can combine KBinsDiscretizer with ColumnTransformer if you only want to preprocess part of the features. KBinsDiscretizer might produce constant features (e.g., when encode = 'onehot' and certain bins do not contain any data). These features can be removed with feature selection algorithms (e.g., VarianceThreshold). Examples >>> X = [[-2, 1, -4,   -1],
...      [-1, 2, -3, -0.5],
...      [ 0, 3, -2,  0.5],
...      [ 1, 4, -1,    2]]
>>> est = KBinsDiscretizer(n_bins=3, encode='ordinal', strategy='uniform')
>>> est.fit(X)
KBinsDiscretizer(...)
>>> Xt = est.transform(X)
>>> Xt  
array([[ 0., 0., 0., 0.],
       [ 1., 1., 1., 0.],
       [ 2., 2., 2., 1.],
       [ 2., 2., 2., 2.]])
 Sometimes it may be useful to convert the data back into the original feature space. The inverse_transform function converts the binned data into the original feature space. Each value will be equal to the mean of the two bin edges. >>> est.bin_edges_[0]
array([-2., -1.,  0.,  1.])
>>> est.inverse_transform(Xt)
array([[-1.5,  1.5, -3.5, -0.5],
       [-0.5,  2.5, -2.5, -0.5],
       [ 0.5,  3.5, -1.5,  0.5],
       [ 0.5,  3.5, -1.5,  1.5]])
 Methods  
fit(X[, y]) Fit the estimator.  
fit_transform(X[, y]) Fit to data, then transform it.  
get_params([deep]) Get parameters for this estimator.  
inverse_transform(Xt) Transform discretized data back to original feature space.  
set_params(**params) Set the parameters of this estimator.  
transform(X) Discretize the data.    
fit(X, y=None) [source]
 
Fit the estimator.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Data to be discretized.  
yNone 

Ignored. This parameter exists only for compatibility with Pipeline.    Returns 
 self
   
  
fit_transform(X, y=None, **fit_params) [source]
 
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Input samples.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None 

Target values (None for unsupervised transformations).  
**fit_paramsdict 

Additional fit parameters.    Returns 
 
X_newndarray array of shape (n_samples, n_features_new) 

Transformed array.     
  
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.     
  
inverse_transform(Xt) [source]
 
Transform discretized data back to original feature space. Note that this function does not regenerate the original data due to discretization rounding.  Parameters 
 
Xtarray-like of shape (n_samples, n_features) 

Transformed data in the binned space.    Returns 
 
Xinvndarray, dtype={np.float32, np.float64} 

Data in the original feature space.     
  
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]
 
Discretize the data.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Data to be discretized.    Returns 
 
Xt{ndarray, sparse matrix}, dtype={np.float32, np.float64} 

Data in the binned space. Will be a sparse matrix if self.encode='onehot' and ndarray otherwise.     
 
 Examples using sklearn.preprocessing.KBinsDiscretizer
 
  Poisson regression and non-normal loss  

  Tweedie regression on insurance claims  

  Using KBinsDiscretizer to discretize continuous features  

  Demonstrating the different strategies of KBinsDiscretizer  

  Feature discretization