from keras.models import Sequential from keras.layers import Dense from keras.wrappers.scikit_learn import KerasClassifier from sklearn.metrics import accuracy_score from sklearn.datasets import make_classification # Create a Keras model (in this case, a simple MLP) 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 # Create a Scikit-learn estimator from the Keras model estimator = KerasClassifier(build_fn=create_model, epochs=10, batch_size=10) # Generate a dataset for testing X, y = make_classification(n_samples=1000, n_classes=2) # Fit the estimator to the dataset estimator.fit(X, y) # Use the estimator to predict classes in the dataset y_pred = estimator.predict(X) # Calculate accuracy accuracy = accuracy_score(y, y_pred) print("Accuracy: %.2f%%" % (accuracy * 100.0))In this example, we create a simple MLP using Keras, wrap it in a Scikit-learn estimator using `KerasClassifier`, fit this estimator to a dataset using `fit`, and then use its `predict` method to make class predictions. We also use Scikit-learn's `accuracy_score` function to calculate the accuracy of these predictions compared to the true classes in the dataset. From the code examples, it can be determined that `keras.wrappers.scikit_learn` is a Python package library that allows for integration of Keras models with the Scikit-learn API.