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src/s/c/scikit-learn-0.14.1/examples/tree/plot_tree_regression_multioutput.py   scikit-learn(Download)
 
# 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=8)

src/s/c/scikit-learn-0.14.1/examples/tree/plot_tree_regression.py   scikit-learn(Download)
 
# Fit regression model
from sklearn.tree import DecisionTreeRegressor
 
clf_1 = DecisionTreeRegressor(max_depth=2)
clf_2 = DecisionTreeRegressor(max_depth=5)

src/s/c/scikit-learn-0.14.1/examples/ensemble/plot_adaboost_regression.py   scikit-learn(Download)
 
# Fit regression model
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import AdaBoostRegressor
 
clf_1 = DecisionTreeRegressor(max_depth=4)
 
clf_2 = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),

src/s/k/sklearn_tutorial-HEAD/examples/plot_sdss_photoz.py   sklearn_tutorial(Download)
 
from sklearn.datasets import get_data_home
from sklearn.tree import DecisionTreeRegressor
 
DATA_URL = ('http://www.astro.washington.edu/users/'
 
 
clf = DecisionTreeRegressor(max_depth=20)
clf.fit(Xtrain, ztrain)
zpred = clf.predict(Xtest)

src/a/s/astroML-0.2/book_figures/chapter9/fig_photoz_tree.py   astroML(Download)
from matplotlib import pyplot as plt
 
from sklearn.tree import DecisionTreeRegressor
from astroML.datasets import fetch_sdss_specgals
 
 
for i, d in enumerate(depth):
    clf = DecisionTreeRegressor(max_depth=d, random_state=0)
    clf.fit(mag_train, z_train)
 

src/a/s/astroML-HEAD/book_figures/chapter9/fig_photoz_tree.py   astroML(Download)
from matplotlib import pyplot as plt
 
from sklearn.tree import DecisionTreeRegressor
from astroML.datasets import fetch_sdss_specgals
 
 
for i, d in enumerate(depth):
    clf = DecisionTreeRegressor(max_depth=d, random_state=0)
    clf.fit(mag_train, z_train)
 

src/s/k/skll-0.23.1/skll/learner.py   skll(Download)
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC, SVC, SVR
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 
from skll.data import ExamplesTuple
class RescaledDecisionTreeRegressor(DecisionTreeRegressor):
    pass
 
 
@rescaled

src/s/k/skll-HEAD/skll/learner.py   skll(Download)
from sklearn.naive_bayes import MultinomialNB
from sklearn.svm import LinearSVC, SVC, SVR
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
 
from skll.data import ExamplesTuple
class RescaledDecisionTreeRegressor(DecisionTreeRegressor):
    pass
 
 
@rescaled

src/s/k/sklearn_pycon2014-HEAD/notebooks/solutions/06-5_decisiontree.py   sklearn_pycon2014(Download)
from sklearn.tree import DecisionTreeRegressor
X, y = make_data(500, error=1)
 
clf = DecisionTreeRegressor()
max_depth = np.arange(1, 10)

src/a/s/astroML-0.2/book_figures/chapter9/fig_photoz_bagging.py   astroML(Download)
from matplotlib import pyplot as plt
 
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor
 

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