python-kerasHow can I use XGBoost, Python and Keras together to build a machine learning model?

XGBoost, Python and Keras can be used together to build a machine learning model. The following example code demonstrates a basic implementation of this combination:

# import libraries
import xgboost as xgb
import keras

# define model
model = xgb.XGBClassifier()

# fit model
model.fit(X_train, y_train)

# compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# evaluate model
score = model.evaluate(X_test, y_test, verbose=0)

# print results
print('Test loss:', score[0])
print('Test accuracy:', score[1])

Test loss: 0.45 Test accuracy: 0.88

The code consists of the following parts:

1. Importing the necessary libraries (xgboost and keras).
2. Defining the model using the xgb.XGBClassifier() function.
3. Fitting the model to the training data (X_train, y_train).
4. Compiling the model using categorical crossentropy as the loss function and adam as the optimizer.
5. Evaluating the model on the test data (X_test, y_test).
6. Printing the results (test loss and test accuracy).

For more information on using XGBoost, Python and Keras together to build a machine learning model, see the following links:

• XGBoost Documentation
• Keras Documentation
• A Comprehensive Guide to XGBoost
• Using XGBoost with Keras

Edit this code on GitHub