98 lines
2.5 KiB
Python
98 lines
2.5 KiB
Python
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"""
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============================================================
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Comparing random forests and the multi-output meta estimator
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============================================================
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An example to compare multi-output regression with random forest and
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the :ref:`multioutput.MultiOutputRegressor <multiclass>` meta-estimator.
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This example illustrates the use of the
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:ref:`multioutput.MultiOutputRegressor <multiclass>` meta-estimator
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to perform multi-output regression. A random forest regressor is used,
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which supports multi-output regression natively, so the results can be
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compared.
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The random forest regressor will only ever predict values within the
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range of observations or closer to zero for each of the targets. As a
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result the predictions are biased towards the centre of the circle.
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Using a single underlying feature the model learns both the
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x and y coordinate as output.
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"""
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# Author: Tim Head <betatim@gmail.com>
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#
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# License: BSD 3 clause
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.multioutput import MultiOutputRegressor
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# Create a random dataset
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rng = np.random.RandomState(1)
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X = np.sort(200 * rng.rand(600, 1) - 100, axis=0)
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y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
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y += 0.5 - rng.rand(*y.shape)
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X_train, X_test, y_train, y_test = train_test_split(
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X, y, train_size=400, test_size=200, random_state=4
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)
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max_depth = 30
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regr_multirf = MultiOutputRegressor(
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RandomForestRegressor(n_estimators=100, max_depth=max_depth, random_state=0)
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)
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regr_multirf.fit(X_train, y_train)
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regr_rf = RandomForestRegressor(n_estimators=100, max_depth=max_depth, random_state=2)
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regr_rf.fit(X_train, y_train)
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# Predict on new data
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y_multirf = regr_multirf.predict(X_test)
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y_rf = regr_rf.predict(X_test)
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# Plot the results
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plt.figure()
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s = 50
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a = 0.4
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plt.scatter(
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y_test[:, 0],
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y_test[:, 1],
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edgecolor="k",
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c="navy",
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s=s,
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marker="s",
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alpha=a,
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label="Data",
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)
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plt.scatter(
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y_multirf[:, 0],
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y_multirf[:, 1],
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edgecolor="k",
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c="cornflowerblue",
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s=s,
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alpha=a,
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label="Multi RF score=%.2f" % regr_multirf.score(X_test, y_test),
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)
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plt.scatter(
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y_rf[:, 0],
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y_rf[:, 1],
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edgecolor="k",
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c="c",
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s=s,
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marker="^",
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alpha=a,
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label="RF score=%.2f" % regr_rf.score(X_test, y_test),
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)
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plt.xlim([-6, 6])
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plt.ylim([-6, 6])
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plt.xlabel("target 1")
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plt.ylabel("target 2")
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plt.title("Comparing random forests and the multi-output meta estimator")
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plt.legend()
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plt.show()
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