from sklearn.tree import DecisionTreeClassifier
f = [[150, 1], [180, 1], [120, 0], [100, 0], [250, 2], [300, 2]]
l = ['A', 'A', 'O', 'O', 'W', 'W']
clf = DecisionTreeClassifier()
t = clf.fit(f, l)
r = t.predict([[180, 1], [120, 0], [150, 1], [130, 0], [250, 2], [300, 2]])
print(r)

bc56501038183bd77b9c13520622fcad79d65f5a
<mask token>

<mask token>
print(r)

<mask token>
f = [[150, 1], [180, 1], [120, 0], [100, 0], [250, 2], [300, 2]]
l = ['A', 'A', 'O', 'O', 'W', 'W']
clf = DecisionTreeClassifier()
t = clf.fit(f, l)
r = t.predict([[180, 1], [120, 0], [150, 1], [130, 0], [250, 2], [300, 2]])
print(r)

from sklearn.tree import DecisionTreeClassifier
f = [[150, 1], [180, 1], [120, 0], [100, 0], [250, 2], [300, 2]]
l = ['A', 'A', 'O', 'O', 'W', 'W']
clf = DecisionTreeClassifier()
t = clf.fit(f, l)
r = t.predict([[180, 1], [120, 0], [150, 1], [130, 0], [250, 2], [300, 2]])
print(r)
