added multi-ouput tree example

Author: bdholt1

examples/tree/plot_tree_regression_multioutput.py

@@ -0,0 +1,53 @@
+"""
+===================================================================
+Decision Tree Regression
+===================================================================
+
+1D multi-output regression with :ref:`decision trees <tree>`: the decision tree
+is used to predict simultaneously the noisy x and y observations of a circle given
+the underlying feature. As a result, it learns local linear regressions 
+approximating the sine curve.
+
+We can see that if the maximum depth of the tree (controlled by the
+`max_depth` parameter) is set too high, the decision trees learn too fine
+details of the training data and learn from the noise, i.e. they overfit.
+"""
+print __doc__
+
+import numpy as np
+
+# Create a random dataset
+rng = np.random.RandomState(1)
+X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
+y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
+y[::5,:] += 3 * (0.5 - rng.rand(20,2))
+
+# Fit regression model
+from sklearn.tree import DecisionTreeRegressor
+
+clf_1 = DecisionTreeRegressor(max_depth=2)
+clf_2 = DecisionTreeRegressor(max_depth=5)
+clf_3 = DecisionTreeRegressor(max_depth=10)
+clf_1.fit(X, y)
+clf_2.fit(X, y)
+clf_3.fit(X, y)
+
+# Predict
+X_test = np.arange(-100.0, 100.0, 0.01)[:, np.newaxis]
+y_1 = clf_1.predict(X_test)
+y_2 = clf_2.predict(X_test)
+y_3 = clf_3.predict(X_test)
+
+# Plot the results
+import pylab as pl
+
+pl.figure()
+pl.scatter(y[:,0], y[:,1], c="k", label="data")
+pl.scatter(y_1[:,0], y_1[:,1], c="g", label="max_depth=2", linewidth=2)
+pl.scatter(y_2[:,0], y_2[:,1], c="r", label="max_depth=5", linewidth=2)
+pl.scatter(y_3[:,0], y_3[:,1], c="b", label="max_depth=10", linewidth=2)
+pl.xlabel("data")
+pl.ylabel("target")
+pl.title("Multi-output Decision Tree Regression")
+pl.legend()
+pl.show()