Please help! Thank you!

I am building an ensemble learner of Random Forest and Artificial Neuro Networks.

The function seems to be able to pass Random Forest to cross_val_predict fine.

However, there are errors with passing the MLPClassifier to cross_val_predict.

Why my function produces those errors?

What do the errors mean?

**Code**

```
def cross_val_model(model, X, Y, n_splits_n=25, n_repeats_n=5, random_state_n=42):
np.random.seed(33)
k = RepeatedStratifiedKFold(n_splits=n_splits_n, n_repeats=n_repeats_n, random_state=random_state_n)
y_preds = cross_val_predict(model, X=X, y=Y, cv=k, n_jobs=-1, method='predict', verbose=1)
return y_preds
def ensemble_fun (X_train, X_test, Y_train, hidden_layer_sizes_n=50, max_iter_n=150, n_estimators_n=100, cv_n=10, max_depth_n=25,random_state_ann=42, random_state_rf=42):
# Scale and center the data around the mean of 0
# scaling=StandardScaler()
# Initialize the log variable
class log:
def_tz = pytz.timezone('America/New_York')
def info(text):
print(f'{datetime.now(log.def_tz).replace(microsecond=0)} : {text}');
# Enumerate list of estimators
estimator_list = {
'ann': MLPClassifier( solver='lbfgs', hidden_layer_sizes=hidden_layer_sizes_n,
max_iter=max_iter_n, shuffle=True, random_state=random_state_ann, activation='logistic'),
'rf': RandomForestClassifier( max_depth=max_depth_n, n_estimators=n_estimators_n, random_state=random_state_rf, n_jobs=-1 )
}
clf = StackingClassifier(estimators=estimator_list, final_estimator=LogisticRegression(), cv=cv_n)
stacking_train_dataset = np.zeros([X_train.shape[0], len(estimator_list)])
stacking_test_dataset = np.zeros([X_train.shape[0], len(estimator_list)])
for i, base_algorithm in enumerate(estimator_list):
stacking_train_dataset[:,i] = cross_val_model(base_algorithm, X_train, Y_train, n_splits_n=25, n_repeats_n=5, random_state_n=42)
stacking_test_dataset[:,i] = base_algorithm.predict(X_test)
final_predictions = clf.fit(stacking_train_dataset, Y_train).predict(stacking_test_dataset)
print(f'Accuracy: {metrics.accuracy_score(stacking_test_dataset, final_predictions)}');
return final_predictions
```

**Calling function ensemble_fun**

```
ensemble_fun (NN_103_score_df_x_train, NN_103_score_df_x_test, NN_103_score_df_y_train, hidden_layer_sizes_n=50, max_iter_n=150, n_estimators_n=100, cv_n=10, max_depth_n=25,random_state_ann=42, random_state_rf=42)
```

**Error Message**

```
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.
---------------------------------------------------------------------------
Empty Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/joblib/parallel.py in dispatch_one_batch(self, iterator)
819 try:
--> 820 tasks = self._ready_batches.get(block=False)
821 except queue.Empty:
8 frames
Empty:
During handling of the above exception, another exception occurred:
TypeError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/sklearn/base.py in clone(estimator, safe)
65 "it does not seem to be a scikit-learn estimator "
66 "as it does not implement a 'get_params' methods."
---> 67 % (repr(estimator), type(estimator)))
68 klass = estimator.__class__
69 new_object_params = estimator.get_params(deep=False)
TypeError: Cannot clone object ''ann'' (type <class 'str'>): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.
```