`sklearn.isotonic`.IsotonicRegression¶

class sklearn.isotonic.IsotonicRegression(*, y_min=None, y_max=None, increasing=True, out_of_bounds='nan')[source]¶

Isotonic regression model.

See also

sklearn.linear_model.LinearRegression: Ordinary least squares Linear Regression.
sklearn.ensemble.HistGradientBoostingRegressor: Gradient boosting that is a non-parametric model accepting monotonicity constraints.
isotonic_regression: Function to solve the isotonic regression model.

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_feature_names_out`([input_features])	Get output feature names for transformation.
`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 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_feature_names_out(input_features=None)[source]¶

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None: Ignored.

Returns:

feature_names_outndarray of str objects: An ndarray with one string i.e. [“isotonicregression0”].

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 of the prediction.

The coefficient of determination \(R^2\) 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

sklearn.isotonic.IsotonicRegression¶

Examples using sklearn.isotonic.IsotonicRegression¶

`sklearn.isotonic`.IsotonicRegression¶

Examples using `sklearn.isotonic.IsotonicRegression`¶