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from sklearn.externals.six import StringIO | |
from IPython.display import Image | |
from sklearn.tree import export_graphviz | |
import pydotplus | |
import os | |
os.environ['PATH'] = os.environ['PATH']+';'+os.environ['CONDA_PREFIX']+r"\Library\bin\graphviz" | |
dot_data = StringIO() | |
export_graphviz(pipe.named_steps['regressor'].estimators_[0], out_file=dot_data) | |
graph = pydotplus.graph_from_dot_data(dot_data.getvalue()) |
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conda install pydotplus | |
conda install graphviz |
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regr = RandomForestRegressor(random_state = 100,bootstrap = True, max_depth=2,max_features=2,min_samples_leaf=3,min_samples_split=5,n_estimators=3) | |
pipe = Pipeline([ | |
('scaler', StandardScaler()), | |
('reduce_dim', PCA()), | |
('regressor', regr) | |
]) | |
pipe.fit(X_train,y_train) | |
ypipe=pipe.predict(X_test) |
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X = df[['GDP','UnemploymentRate']] | |
y = df['Revenue'] | |
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0) |
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