81 lines
2.6 KiB
Python
81 lines
2.6 KiB
Python
|
"""
|
||
|
|
======================================
|
||
|
|
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.
|
||
|
|
|
||
|
|
# Author: Noel Dawe <noel.dawe@gmail.com>
|
||
|
|
#
|
||
|
|
# License: 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()
|