Lab 2 - Decision Trees

1 - Check we have required libraries



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# Pandas Data Library
!pip install --upgrade pandas



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# Scikit-Learn
!pip install --upgrade sklearn



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# Install Graphviz visualization
!sudo apt install python-pydot python-pydot-ng graphviz



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# Pydotplus visualization
!pip install --upgrade pydotplus

2 - Load iris flower data



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import sklearn.datasets as datasets
import pandas as pd



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iris=datasets.load_iris()
df=pd.DataFrame(iris.data, columns=iris.feature_names)
y=iris.target

3 - Define decision tree classifier



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from sklearn.tree import DecisionTreeClassifier
dtree=DecisionTreeClassifier()
dtree.fit(df,y)

4 - Create visualization plot



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from sklearn.externals.six import StringIO  
from IPython.display import Image  
from sklearn.tree import export_graphviz
import pydotplus



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dot_data = StringIO()
export_graphviz(dtree, out_file=dot_data,  
                filled=True, rounded=True,
                special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  
Image(graph.create_png())