.. _sphx_glr_auto_examples_ensemble_plot_random_forest_regression_multioutput.py: ============================================================ Comparing random forests and the multi-output meta estimator ============================================================ An example to compare multi-output regression with random forest and the :ref:`multioutput.MultiOutputRegressor ` meta-estimator. This example illustrates the use of the :ref:`multioutput.MultiOutputRegressor ` meta-estimator to perform multi-output regression. A random forest regressor is used, which supports multi-output regression natively, so the results can be compared. The random forest regressor will only ever predict values within the range of observations or closer to zero for each of the targets. As a result the predictions are biased towards the centre of the circle. Using a single underlying feature the model learns both the x and y coordinate as output. .. image:: /auto_examples/ensemble/images/sphx_glr_plot_random_forest_regression_multioutput_001.png :align: center .. code-block:: python print(__doc__) # Author: Tim Head # # License: BSD 3 clause import numpy as np import matplotlib.pyplot as plt from sklearn.ensemble import RandomForestRegressor from sklearn.model_selection import train_test_split from sklearn.multioutput import MultiOutputRegressor # Create a random dataset rng = np.random.RandomState(1) X = np.sort(200 * rng.rand(600, 1) - 100, axis=0) y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T y += (0.5 - rng.rand(*y.shape)) X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=400, random_state=4) max_depth = 30 regr_multirf = MultiOutputRegressor(RandomForestRegressor(max_depth=max_depth, random_state=0)) regr_multirf.fit(X_train, y_train) regr_rf = RandomForestRegressor(max_depth=max_depth, random_state=2) regr_rf.fit(X_train, y_train) # Predict on new data y_multirf = regr_multirf.predict(X_test) y_rf = regr_rf.predict(X_test) # Plot the results plt.figure() s = 50 a = 0.4 plt.scatter(y_test[:, 0], y_test[:, 1], c="navy", s=s, marker="s", alpha=a, label="Data") plt.scatter(y_multirf[:, 0], y_multirf[:, 1], c="cornflowerblue", s=s, alpha=a, label="Multi RF score=%.2f" % regr_multirf.score(X_test, y_test)) plt.scatter(y_rf[:, 0], y_rf[:, 1], c="c", s=s, marker="^", alpha=a, label="RF score=%.2f" % regr_rf.score(X_test, y_test)) plt.xlim([-6, 6]) plt.ylim([-6, 6]) plt.xlabel("target 1") plt.ylabel("target 2") plt.title("Comparing random forests and the multi-output meta estimator") plt.legend() plt.show() **Total running time of the script:** (0 minutes 0.213 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_random_forest_regression_multioutput.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_random_forest_regression_multioutput.ipynb `