I am trying to execute the below code for KFold cross validation:
def evaluate_model(X, Y):
results = list()
n_inputs, n_outputs = X.shape[1], Y.shape[1]
# define evaluation procedure
cv = RepeatedKFold(n_splits=10, n_repeats=3, random_state=1)
# enumerate folds
for train_ix, test_ix in cv.split(X):
# prepare data
X_train, X_test = X.iloc[list(train_ix)], X.iloc[list(test_ix)]
y_train, y_test = Y.iloc[list(train_ix)], Y.iloc[list(test_ix)]
# define model
model = get_model(n_inputs, n_outputs)
# fit model
model.fit(X_train, y_train, verbose=0, epochs=100)
# make a prediction on the test set
yhat = model.predict(X_test)
# round probabilities to class labels
yhat = yhat.round()
# calculate accuracy
acc = accuracy_score(y_test, yhat)
# store result
print('>%.3f' % acc)
results.append(acc)
return results
results = evaluate_model(X, Y)
by running the code I am getting the below error:
y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\ProgramData\Anaconda3\lib\site-packages\sklearn\metrics\classification.py", line 99, in _check_targets
y_true = csr_matrix(y_true)
File "C:\Users\XYZ\AppData\Roaming\Python\Python37\site-packages\scipy\sparse\compressed.py", line 88, in __init__
self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
File "C:\Users\XYZ\AppData\Roaming\Python\Python37\site-packages\scipy\sparse\compressed.py", line 88, in __init__
self._set_self(self.__class__(coo_matrix(arg1, dtype=dtype)))
File "C:\Users\XYZ\AppData\Roaming\Python\Python37\site-packages\scipy\sparse\coo.py", line 191, in __init__
self.row, self.col = M.nonzero()
File "C:\Users\XYZ\AppData\Roaming\Python\Python37\site-packages\scipy\sparse\base.py", line 287, in __bool__
raise ValueError("The truth value of an array with more than one "
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all().
What is causing the error and how to resolve this?
