sklearn.linear_model
.QuantileRegressor¶
- class sklearn.linear_model.QuantileRegressor(*, quantile=0.5, alpha=1.0, fit_intercept=True, solver='warn', solver_options=None)[source]¶
Linear regression model that predicts conditional quantiles.
The linear
QuantileRegressor
optimizes the pinball loss for a desiredquantile
and is robust to outliers.This model uses an L1 regularization like
Lasso
.Read more in the User Guide.
New in version 1.0.
- Parameters:
- quantilefloat, default=0.5
The quantile that the model tries to predict. It must be strictly between 0 and 1. If 0.5 (default), the model predicts the 50% quantile, i.e. the median.
- alphafloat, default=1.0
Regularization constant that multiplies the L1 penalty term.
- fit_interceptbool, default=True
Whether or not to fit the intercept.
- solver{‘highs-ds’, ‘highs-ipm’, ‘highs’, ‘interior-point’, ‘revised simplex’}, default=’interior-point’
Method used by
scipy.optimize.linprog
to solve the linear programming formulation.From
scipy>=1.6.0
, it is recommended to use the highs methods because they are the fastest ones. Solvers “highs-ds”, “highs-ipm” and “highs” support sparse input data and, in fact, always convert to sparse csc.From
scipy>=1.11.0
, “interior-point” is not available anymore.Changed in version 1.4: The default of
solver
will change to"highs"
in version 1.4.- solver_optionsdict, default=None
Additional parameters passed to
scipy.optimize.linprog
as options. IfNone
and ifsolver='interior-point'
, then{"lstsq": True}
is passed toscipy.optimize.linprog
for the sake of stability.
- Attributes:
- coef_array of shape (n_features,)
Estimated coefficients for the features.
- intercept_float
The intercept of the model, aka bias term.
- n_features_in_int
Number of features seen during fit.
New in version 0.24.
- feature_names_in_ndarray of shape (
n_features_in_
,) Names of features seen during fit. Defined only when
X
has feature names that are all strings.New in version 1.0.
- n_iter_int
The actual number of iterations performed by the solver.
See also
Lasso
The Lasso is a linear model that estimates sparse coefficients with l1 regularization.
HuberRegressor
Linear regression model that is robust to outliers.
Examples
>>> from sklearn.linear_model import QuantileRegressor >>> import numpy as np >>> n_samples, n_features = 10, 2 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> # the two following lines are optional in practice >>> from sklearn.utils.fixes import sp_version, parse_version >>> solver = "highs" if sp_version >= parse_version("1.6.0") else "interior-point" >>> reg = QuantileRegressor(quantile=0.8, solver=solver).fit(X, y) >>> np.mean(y <= reg.predict(X)) 0.8
Methods
fit
(X, y[, sample_weight])Fit the model according to the given training data.
Get metadata routing of this object.
get_params
([deep])Get parameters for this estimator.
predict
(X)Predict using the linear model.
score
(X, y[, sample_weight])Return the coefficient of determination of the prediction.
set_fit_request
(*[, sample_weight])Request metadata passed to the
fit
method.set_params
(**params)Set the parameters of this estimator.
set_score_request
(*[, sample_weight])Request metadata passed to the
score
method.- fit(X, y, sample_weight=None)[source]¶
Fit the model according to the given training data.
- Parameters:
- X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
- yarray-like of shape (n_samples,)
Target values.
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- Returns:
- selfobject
Returns self.
- get_metadata_routing()[source]¶
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
- Returns:
- routingMetadataRequest
A
MetadataRequest
encapsulating routing information.
- 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(X)[source]¶
Predict using the linear model.
- Parameters:
- Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples.
- Returns:
- Carray, shape (n_samples,)
Returns predicted values.
- 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 ofy
, 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)
, wheren_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)
w.r.t.y
.
Notes
The \(R^2\) score used when calling
score
on a regressor usesmultioutput='uniform_average'
from version 0.23 to keep consistent with default value ofr2_score
. This influences thescore
method of all the multioutput regressors (except forMultiOutputRegressor
).
- set_fit_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') QuantileRegressor [source]¶
Request metadata passed to the
fit
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter infit
.
- Returns:
- selfobject
The updated object.
- 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.
- set_score_request(*, sample_weight: Union[bool, None, str] = '$UNCHANGED$') QuantileRegressor [source]¶
Request metadata passed to the
score
method.Note that this method is only relevant if
enable_metadata_routing=True
(seesklearn.set_config
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed toscore
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it toscore
.None
: metadata is not requested, and the meta-estimator will raise an error if the user provides it.str
: metadata should be passed to the meta-estimator with this given alias instead of the original name.
The default (
sklearn.utils.metadata_routing.UNCHANGED
) retains the existing request. This allows you to change the request for some parameters and not others.New in version 1.3.
Note
This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a
Pipeline
. Otherwise it has no effect.- Parameters:
- sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED
Metadata routing for
sample_weight
parameter inscore
.
- Returns:
- selfobject
The updated object.