sklearn.isotonic.IsotonicRegression  
class sklearn.isotonic.IsotonicRegression(*, y_min=None, y_max=None, increasing=True, out_of_bounds='nan') [source]
 
Isotonic regression model. Read more in the User Guide.  New in version 0.13.   Parameters 
 
y_minfloat, default=None 

Lower bound on the lowest predicted value (the minimum value may still be higher). If not set, defaults to -inf.  
y_maxfloat, default=None 

Upper bound on the highest predicted value (the maximum may still be lower). If not set, defaults to +inf.  
increasingbool or ‘auto’, default=True 

Determines whether the predictions should be constrained to increase or decrease with X. ‘auto’ will decide based on the Spearman correlation estimate’s sign.  
out_of_bounds{‘nan’, ‘clip’, ‘raise’}, default=’nan’ 

Handles how X values outside of the training domain are handled during prediction.  ‘nan’, predictions will be NaN. ‘clip’, predictions will be set to the value corresponding to the nearest train interval endpoint. ‘raise’, a ValueError is raised.     Attributes 
 
X_min_float 

Minimum value of input array X_ for left bound.  
X_max_float 

Maximum value of input array X_ for right bound.  
X_thresholds_ndarray of shape (n_thresholds,) 

Unique ascending X values used to interpolate the y = f(X) monotonic function.  New in version 0.24.   
y_thresholds_ndarray of shape (n_thresholds,) 

De-duplicated y values suitable to interpolate the y = f(X) monotonic function.  New in version 0.24.   
f_function 

The stepwise interpolating function that covers the input domain X.  
increasing_bool 

Inferred value for increasing.     Notes Ties are broken using the secondary method from de Leeuw, 1977. References Isotonic Median Regression: A Linear Programming Approach Nilotpal Chakravarti Mathematics of Operations Research Vol. 14, No. 2 (May, 1989), pp. 303-308 Isotone Optimization in R : Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods de Leeuw, Hornik, Mair Journal of Statistical Software 2009 Correctness of Kruskal’s algorithms for monotone regression with ties de Leeuw, Psychometrica, 1977 Examples >>> from sklearn.datasets import make_regression
>>> from sklearn.isotonic import IsotonicRegression
>>> X, y = make_regression(n_samples=10, n_features=1, random_state=41)
>>> iso_reg = IsotonicRegression().fit(X, y)
>>> iso_reg.predict([.1, .2])
array([1.8628..., 3.7256...])
 Methods  
fit(X, y[, sample_weight]) Fit the model using X, y as training data.  
fit_transform(X[, y]) Fit to data, then transform it.  
get_params([deep]) Get parameters for this estimator.  
predict(T) Predict new data by linear interpolation.  
score(X, y[, sample_weight]) Return the coefficient of determination \(R^2\) of the prediction.  
set_params(**params) Set the parameters of this estimator.  
transform(T) Transform new data by linear interpolation    
fit(X, y, sample_weight=None) [source]
 
Fit the model using X, y as training data.  Parameters 
 
Xarray-like of shape (n_samples,) or (n_samples, 1) 

Training data.  Changed in version 0.24: Also accepts 2d array with 1 feature.   
yarray-like of shape (n_samples,) 

Training target.  
sample_weightarray-like of shape (n_samples,), default=None 

Weights. If set to None, all weights will be set to 1 (equal weights).    Returns 
 
selfobject 

Returns an instance of self.     Notes X is stored for future use, as transform needs X to interpolate new input data. 
  
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.     
  
predict(T) [source]
 
Predict new data by linear interpolation.  Parameters 
 
Tarray-like of shape (n_samples,) or (n_samples, 1) 

Data to transform.    Returns 
 
y_predndarray of shape (n_samples,) 

Transformed data.     
  
score(X, y, sample_weight=None) [source]
 
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y_true - y_pred)
** 2).sum() and \(v\) is the total sum of squares ((y_true -
y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.  Parameters 
 
Xarray-like of shape (n_samples, n_features) 

Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.  
yarray-like of shape (n_samples,) or (n_samples, n_outputs) 

True values for X.  
sample_weightarray-like of shape (n_samples,), default=None 

Sample weights.    Returns 
 
scorefloat 

\(R^2\) of self.predict(X) wrt. y.     Notes The \(R^2\) score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score. This influences the score method of all the multioutput regressors (except for MultiOutputRegressor). 
  
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(T) [source]
 
Transform new data by linear interpolation  Parameters 
 
Tarray-like of shape (n_samples,) or (n_samples, 1) 

Data to transform.  Changed in version 0.24: Also accepts 2d array with 1 feature.     Returns 
 
y_predndarray of shape (n_samples,) 

The transformed data     
 
 Examples using sklearn.isotonic.IsotonicRegression
 
  Isotonic Regression