def test_textDecisionTree(self):
iris = load_iris()
X = iris.data
y = iris.target
X_train, _, y_train, _ = train_test_split(X, y, random_state=0)
estimator = DecisionTreeClassifier(max_leaf_nodes=3, random_state=0)
estimator.fit(X_train, y_train)
representation = textDecisionTree(estimator)
# print(representation)
self.assertIn('node=0 test node', representation)
self.assertIn('node=1 leaf node', representation)
self.assertIn('node=2 test node', representation)
self.assertIn('node=3 leaf node', representation)
self.assertIn('node=4 leaf node', representation)
predictors = ['borrower_score', 'payment_inc_ratio']
outcome = 'outcome'
X = loan3000[predictors]
y = loan3000[outcome]
loan_tree = DecisionTreeClassifier(random_state=1,
criterion='entropy',
min_impurity_decrease=0.003)
loan_tree.fit(X, y)
plotDecisionTree(loan_tree,
feature_names=predictors,
class_names=loan_tree.classes_)
print(textDecisionTree(loan_tree))
### The Recursive Partitioning Algorithm
fig, ax = plt.subplots(figsize=(6, 4))
loan3000.loc[loan3000.outcome == 'paid off'].plot(x='borrower_score',
y='payment_inc_ratio',
style='.',
markerfacecolor='none',
markeredgecolor='C1',
ax=ax)
loan3000.loc[loan3000.outcome == 'default'].plot(x='borrower_score',
y='payment_inc_ratio',
style='o',
markerfacecolor='none',