examples/ensemble/plot_stack_predictors.py - main - githubmirror/scikit-learn

examples/ensemble/plot_stack_predictors.py
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Charlie Tonneslan
DOC Drop duplicate 'the the' in plot_stack_predictors example comment (#34104)
24 май 2026, 16:34
24 май 2026, 16:34b9a147f
"""
=================================
Combine predictors using stacking
=================================

.. currentmodule:: sklearn

Stacking is an :ref:`ensemble method <ensemble>`. In this strategy, the
out-of-fold predictions from several base estimators are used to train a
meta-model that combines their outputs at inference time. Unlike
:class:`~sklearn.ensemble.VotingRegressor`, which averages predictions with
fixed (optionally user-specified) weights,
:class:`~sklearn.ensemble.StackingRegressor` learns the combination through its
`final_estimator`.

In this example, we illustrate the use case in which different regressors are
stacked together and a final regularized linear regressor is used to output the
prediction. We compare the performance of each individual regressor with the
stacking strategy. Here, stacking slightly improves the overall performance.

"""

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

# %%
# Generate data
# #############
#
# We use synthetic data generated from a sinusoid plus a linear trend with
# heteroscedastic Gaussian noise. A sudden drop is introduced, as it cannot be
# described by a linear model, but a tree-based model can naturally deal with
# it.

import numpy as np
import pandas as pd

rng = np.random.RandomState(42)
X = rng.uniform(-3, 3, size=500)
trend = 2.4 * X
seasonal = 3.1 * np.sin(3.2 * X)
drop = 10.0 * (X > 2).astype(float)
sigma = 0.75 + 0.75 * X**2
y = trend + seasonal - drop + rng.normal(loc=0.0, scale=np.sqrt(sigma))

df = pd.DataFrame({"X": X, "y": y})
_ = df.plot.scatter(x="X", y="y")

# %%
# Stack of predictors on a single data set
# ########################################
#
# It is sometimes not evident which model is more suited for a given task, as
# different model families can achieve similar performance while exhibiting
# different strengths and weaknesses. Stacking combines their outputs to exploit
# these complementary behaviors and can correct systematic errors that no single
# model can fix on its own. With appropriate regularization in the
# `final_estimator`, the :class:`~sklearn.ensemble.StackingRegressor` often
# matches the strongest base model, and can outperform it when base learners'
# errors are only partially correlated, allowing the combination to reduce
# individual bias/variance.
#
# Here, we combine 3 learners (linear and non-linear) and use the default
# :class:`~sklearn.linear_model.RidgeCV` regressor to combine their outputs
# together.
#
# .. note::
#    Although some base learners include preprocessing (such as the
#    :class:`~sklearn.preprocessing.StandardScaler`), the `final_estimator` does
#    not need additional preprocessing when using the default
#    `passthrough=False`, as it receives only the base learners' predictions. If
#    `passthrough=True`, `final_estimator` should be a pipeline with proper
#    preprocessing.

from sklearn.ensemble import HistGradientBoostingRegressor, StackingRegressor
from sklearn.linear_model import RidgeCV
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import PolynomialFeatures, SplineTransformer, StandardScaler

linear_ridge = make_pipeline(StandardScaler(), RidgeCV())

spline_ridge = make_pipeline(
    SplineTransformer(n_knots=6, degree=3),
    PolynomialFeatures(interaction_only=True),
    RidgeCV(),
)

hgbt = HistGradientBoostingRegressor(random_state=0)

estimators = [
    ("Linear Ridge", linear_ridge),
    ("Spline Ridge", spline_ridge),
    ("HGBT", hgbt),
]

stacking_regressor = StackingRegressor(estimators=estimators, final_estimator=RidgeCV())
stacking_regressor

# %%
# Measure and plot the results
# ############################
#
# We can directly plot the predictions. Indeed, the sudden drop is correctly
# described by the :class:`~sklearn.ensemble.HistGradientBoostingRegressor`
# model (HGBT), but the spline model is smoother and less overfitting. The stacked
# regressor then turns to be a smoother version of the HGBT.

import matplotlib.pyplot as plt

X = X.reshape(-1, 1)
linear_ridge.fit(X, y)
spline_ridge.fit(X, y)
hgbt.fit(X, y)
stacking_regressor.fit(X, y)

x_plot = np.linspace(X.min() - 0.1, X.max() + 0.1, 500).reshape(-1, 1)
preds = {
    "Linear Ridge": linear_ridge.predict(x_plot),
    "Spline Ridge": spline_ridge.predict(x_plot),
    "HGBT": hgbt.predict(x_plot),
    "Stacking (Ridge final estimator)": stacking_regressor.predict(x_plot),
}

fig, axes = plt.subplots(2, 2, figsize=(10, 8), sharex=True, sharey=True)
axes = axes.ravel()
for ax, (name, y_pred) in zip(axes, preds.items()):
    ax.scatter(
        X[:, 0],
        y,
        s=6,
        alpha=0.35,
        linewidths=0,
        label="observed (sample)",
    )

    ax.plot(x_plot.ravel(), y_pred, linewidth=2, alpha=0.9, label=name)
    ax.set_title(name)
    ax.set_xlabel("x")
    ax.set_ylabel("y")
    ax.legend(loc="lower right")

plt.suptitle("Base Models Predictions versus Stacked Predictions", y=1)
plt.tight_layout()
plt.show()

# %%
# We can plot the prediction errors as well and evaluate the performance of the
# individual predictors and the stack of the regressors.

import time

from sklearn.metrics import PredictionErrorDisplay
from sklearn.model_selection import cross_val_predict, cross_validate

fig, axs = plt.subplots(2, 2, figsize=(9, 7))
axs = np.ravel(axs)

for ax, (name, est) in zip(
    axs, estimators + [("Stacking Regressor", stacking_regressor)]
):
    scorers = {r"$R^2$": "r2", "MAE": "neg_mean_absolute_error"}

    start_time = time.time()
    scores = cross_validate(est, X, y, scoring=list(scorers.values()), n_jobs=-1)
    elapsed_time = time.time() - start_time

    y_pred = cross_val_predict(est, X, y, n_jobs=-1)
    scores = {
        key: (
            f"{np.abs(np.mean(scores[f'test_{value}'])):.2f}"
            r" $\pm$ "
            f"{np.std(scores[f'test_{value}']):.2f}"
        )
        for key, value in scorers.items()
    }

    display = PredictionErrorDisplay.from_predictions(
        y_true=y,
        y_pred=y_pred,
        kind="actual_vs_predicted",
        ax=ax,
        scatter_kwargs={"alpha": 0.2, "color": "tab:blue"},
        line_kwargs={"color": "tab:red"},
    )
    ax.set_title(f"{name}\nEvaluation in {elapsed_time:.2f} seconds")

    for name, score in scores.items():
        ax.plot([], [], " ", label=f"{name}: {score}")
    ax.legend(loc="upper left")

plt.suptitle("Prediction Errors of Base versus Stacked Predictors", y=1)
plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.show()

# %%
# Even if the scores overlap considerably after cross-validation, the predictions
# from the stacked regressor are slightly better.
#
# Once fitted, we can inspect the coefficients (or meta-weights) of the trained
# `final_estimator_` (as long as it is a linear model). They reveal how much the
# individual estimators contribute to the stacked regressor:

stacking_regressor.fit(X, y)
stacking_regressor.final_estimator_.coef_

# %%
# We see that in this case, the HGBT model dominates, with the spline
# ridge also contributing meaningfully. The plain linear model does not add
# useful signal once those two are included; with
# :class:`~sklearn.linear_model.RidgeCV` as the `final_estimator`, it is not
# dropped, but receives a small negative weight to correct its residual bias.
#
# If we use :class:`~sklearn.linear_model.LassoCV` as the
# `final_estimator`, that small, unhelpful contribution is set exactly to zero,
# yielding a simpler blend of the spline ridge and HGBT models.

from sklearn.linear_model import LassoCV

stacking_regressor = StackingRegressor(estimators=estimators, final_estimator=LassoCV())
stacking_regressor.fit(X, y)
stacking_regressor.final_estimator_.coef_

# %%
# How to mimic SuperLearner with scikit-learn
# ###########################################
#
# The `SuperLearner` [Polley2010]_ is a stacking strategy implemented as `an R
# package <https://cran.r-project.org/web/packages/SuperLearner/index.html>`_, but
# not available off-the-shelf in Python. It is closely related to the
# :class:`~sklearn.ensemble.StackingRegressor`, as both train the meta-model on
# out-of-fold predictions from the base estimators.
#
# The key difference is that `SuperLearner` estimates a convex set of
# meta-weights (non-negative and summing to 1) and omits an intercept; by
# contrast, :class:`~sklearn.ensemble.StackingRegressor` uses an unconstrained
# meta-learner with an intercept by default (and can optionally include raw
# features via passthrough).
#
# Without an intercept, the meta-weights are directly interpretable as
# fractional contributions to the final prediction.

from sklearn.linear_model import LinearRegression

linear_reg = LinearRegression(fit_intercept=False, positive=True)
super_learner_like = StackingRegressor(
    estimators=estimators, final_estimator=linear_reg
)
super_learner_like.fit(X, y)
super_learner_like.final_estimator_.coef_

# %%
# The sum of meta-weights in the stacked regressor is close to 1.0, but not
# exactly one:

super_learner_like.final_estimator_.coef_.sum()

# %%
# Beyond interpretability, the normalization to 1.0 constraint in the `SuperLearner`
# presents the following advantages:
#
# - Consensus-preserving: if all base models output the same value at a point,
#   the ensemble returns that same value (no artificial amplification or
#   attenuation).
# - Translation-equivariant: adding a constant to every base prediction shifts
#   the ensemble by the same constant.
# - Removes one degree of freedom: avoiding redundancy with a constant term and
#   modestly stabilizing weights under collinearity.
#
# The cleanest way to enforce the coefficient normalization with scikit-learn is
# by defining a custom estimator, but doing so is beyond the scope of this
# tutorial.
#
# Conclusions
# ###########
#
# The stacked regressor combines the strengths of the different regressors.
# However, notice that training the stacked regressor is much more
# computationally expensive than selecting the best performing model.
#
# .. rubric:: References
#
# .. [Polley2010] Polley, E. C. and van der Laan, M. J., `Super Learner In
#    Prediction
#    <https://biostats.bepress.com/cgi/viewcontent.cgi?article=1269&context=ucbbiostat>`_,
#    2010.