ValueError: could not broadcast input array from shape (2712,1) into shape (2712,)

Question

I am building a neural network model for a classification problem that determines whether a customer will churn or not, and the output is a binary 0 and 1. I also used Random Forest Model and XGboost model. They all worked. I combined the random forest with XGBoost, and it worked fine.

However, when I combined the random forest, XGBoost , with the neural network (Keras classifier) using the voting classifier, I got the error ValueError: could not broadcast input array from the shape (2712,1) into shape (2712,)

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import numpy


 
# Function to create model, required for KerasClassifier
def create_model():
    # create model
    model = Sequential()
    model.add(Dense(12, input_dim=17, activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1,activation='sigmoid'))
# Compile model
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model


# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)

# create model


Kc_model = KerasClassifier(build_fn=create_model)
#model.set_params(epochs=100, batch_size=10, verbose=0)
Kc_model._estimator_type = "classifier"

Kc_model.fit(X_train, y_train, epochs=100,batch_size=10)


print("The accuracy score for Keras Model is")
print("Test set: {}%".format(round(Kc_model.score(X_test, y_test)*100))) 

The code for the voting classifier below:

 from keras.wrappers.scikit_learn import KerasClassifier
import scikeras
from tensorflow import keras


voting = VotingClassifier(
    estimators = [('rf',rf),('xgboost_model',xgboost_model),('Kc_model',Kc_model) ],
    voting='hard')
#reshaping=y_test.reshape(2712,1)
voting_model =voting.fit(X_train, y_train)
voting_pred = voting_model.predict(X_test)
#Model Score
print("The accuracy score for Voting Classifier is")
print("Training:{}%".format(round(voting_model.score(X_train, y_train)*100)))
print("Test set: {}%".format(round(voting_model.score(X_test, y_test)*100)))

Answer 1

It seems like one of your classifiers outputs prediction with shape (2712,1) (2D), and one with shape (2712,) (1D). VotingClassifier can't combine 2D and 1D predictions, so you obtain an error.

I suggest looking carefully at predictions of your classifiers. Try to make them output predictions with the same number of dimensions.