"""
======================================
Decision Tree Regression with AdaBoost
======================================

A decision tree is boosted using the AdaBoost.R2 [1]_ algorithm on a 1D
sinusoidal dataset with a small amount of Gaussian noise.
299 boosts (300 decision trees) is compared with a single decision tree
regressor. As the number of boosts is increased the regressor can fit more
detail.

See :ref:`sphx_glr_auto_examples_ensemble_plot_hgbt_regression.py` for an
example showcasing the benefits of using more efficient regression models such
as :class:`~ensemble.HistGradientBoostingRegressor`.

.. [1] `H. Drucker, "Improving Regressors using Boosting Techniques", 1997.
        <https://citeseerx.ist.psu.edu/doc_view/pid/8d49e2dedb817f2c3330e74b63c5fc86d2399ce3>`_

"""

# %%
# Preparing the data
# ------------------
# First, we prepare dummy data with a sinusoidal relationship and some gaussian noise.

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

import numpy as np

rng = np.random.RandomState(1)
X = np.linspace(0, 6, 100)[:, np.newaxis]
y = np.sin(X).ravel() + np.sin(6 * X).ravel() + rng.normal(0, 0.1, X.shape[0])

# %%
# Training and prediction with DecisionTree and AdaBoost Regressors
# -----------------------------------------------------------------
# Now, we define the classifiers and fit them to the data.
# Then we predict on that same data to see how well they could fit it.
# The first regressor is a `DecisionTreeRegressor` with `max_depth=4`.
# The second regressor is an `AdaBoostRegressor` with a `DecisionTreeRegressor`
# of `max_depth=4` as base learner and will be built with `n_estimators=300`
# of those base learners.

from sklearn.ensemble import AdaBoostRegressor
from sklearn.tree import DecisionTreeRegressor

regr_1 = DecisionTreeRegressor(max_depth=4)

regr_2 = AdaBoostRegressor(
    DecisionTreeRegressor(max_depth=4), n_estimators=300, random_state=rng
)

regr_1.fit(X, y)
regr_2.fit(X, y)

y_1 = regr_1.predict(X)
y_2 = regr_2.predict(X)

# %%
# Plotting the results
# --------------------
# Finally, we plot how well our two regressors,
# single decision tree regressor and AdaBoost regressor, could fit the data.

import matplotlib.pyplot as plt
import seaborn as sns

colors = sns.color_palette("colorblind")

plt.figure()
plt.scatter(X, y, color=colors[0], label="training samples")
plt.plot(X, y_1, color=colors[1], label="n_estimators=1", linewidth=2)
plt.plot(X, y_2, color=colors[2], label="n_estimators=300", linewidth=2)
plt.xlabel("data")
plt.ylabel("target")
plt.title("Boosted Decision Tree Regression")
plt.legend()
plt.show()