error in DecisionTree

[Gewissta]

data = pd.read_csv('Data/Bankloan.csv', sep=';')
for i in ['debtinc', 'creddebt', 'othdebt']:
data[i] = data[i].str.replace(',', '.').astype('float')
train, test, y_train, y_test = train_test_split(data.drop('default', axis=1),
data['default'],
test_size=0.3,
stratify=data['default'],
random_state=42)
X_train = pd.get_dummies(train)
X_test = pd.get_dummies(test)
tree = DecisionTree(seed=42, max_depth=4, n_feats=2)
tree.fit(X_train.values, y_train.values)

---

ValueError Traceback (most recent call last)
in
1 tree = DecisionTree(seed=42, max_depth=4, n_feats=2)
----> 2 tree.fit(X_train.values, y_train.values)

in fit(self, X, Y)
78 self.n_classes = max(Y) + 1 if self.classifier else None
79 self.n_feats = X.shape[1] if not self.n_feats else min(self.n_feats, X.shape[1])
---> 80 self.root = self._grow(X, Y)
81
82 def predict(self, X):

in _grow(self, X, Y, cur_depth)
138
139 # grow the children that result from the split
--> 140 left = self._grow(X[l, :], Y[l], cur_depth)
141 right = self._grow(X[r, :], Y[r], cur_depth)
142 return Node(left, right, (feat, thresh))

in _grow(self, X, Y, cur_depth)
139 # grow the children that result from the split
140 left = self._grow(X[l, :], Y[l], cur_depth)
--> 141 right = self._grow(X[r, :], Y[r], cur_depth)
142 return Node(left, right, (feat, thresh))
143

in _grow(self, X, Y, cur_depth)
139 # grow the children that result from the split
140 left = self._grow(X[l, :], Y[l], cur_depth)
--> 141 right = self._grow(X[r, :], Y[r], cur_depth)
142 return Node(left, right, (feat, thresh))
143

in _grow(self, X, Y, cur_depth)
133
134 # greedily select the best split according to criterion
--> 135 feat, thresh = self._segment(X, Y, feat_idxs)
136 l = np.argwhere(X[:, feat] <= thresh).flatten()
137 r = np.argwhere(X[:, feat] > thresh).flatten()

in _segment(self, X, Y, feat_idxs)
155 gains = np.array([self._impurity_gain(Y, t, vals) for t in thresholds])
156
--> 157 if gains.max() > best_gain:
158 split_idx = i
159 best_gain = gains.max()

/anaconda3/lib/python3.7/site-packages/numpy/core/_methods.py in _amax(a, axis, out, keepdims, initial, where)
28 def _amax(a, axis=None, out=None, keepdims=False,
29 initial=_NoValue, where=True):
---> 30 return umr_maximum(a, axis, None, out, keepdims, initial, where)
31
32 def _amin(a, axis=None, out=None, keepdims=False,

ValueError: zero-size array to reduction operation maximum which has no identity

Link to dataset https://drive.google.com/file/d/1lj7qUyG7BOV6cAGm8-tDNUqS62IEgk5p/view?usp=sharing