from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasClassifier def create_model(): model = Sequential() model.add(Dense(12, input_dim=8, activation='relu')) model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) return model model = KerasClassifier(build_fn=create_model, epochs=10, batch_size=32)In this example, we define a simple Keras neural network model using the Sequential model API. We then define the create_model function which returns the model we just defined. Next, we instantiate the KerasClassifier, passing in the create_model function as the build_fn argument, and the number of epochs and batch size we want to use for training. Overall, we can see that the KerasClassifier is an indispensable tool for integrating deep learning models with Scikit-Learn, allowing us to seamlessly combine the strengths of both libraries.