Welcome to SigOpt Examples. These examples show you how to use SigOpt for model tuning tasks in various machine learning environments.

These examples will run on any Linux or Mac OS X machine from the command line.

If this is your first time using SigOpt, we recommend you work through the Python Text Classifier example. In this example you will create a logistic regression model to classify Amazon product reviews and use SigOpt maximize the k-fold cross-validation accuracy by tuning the regression coefficients and feature parameters.

• ipython-notebook-example: Simple example of using SigOpt to optimize a 2D function with plots and comparisons in an iPython Notebook.
• java: An example of using the Java API client.
• sigopt-beats-vegas: Using SigOpt to tune a model to beat the Vegas odds in Python (blog post).
• text-classifier: Example using SigOpt to tune a text classifier in Python (blog post).
• unsupervised-model: Example using SigOpt and xgboost to tune a combined unsupervised and supervised model for optical character recognition (blog post)
• tensorflow-cnn: Example using SigOpt and TensorFlow to tune a convolutional neural network's structure and gradient descent algorithm (blog post)
• classifier: Using SigOpt to tune a machine learning classifier in Python (blog post).
• parallel: Examples of running SigOpt from multiple parallel processes in Python (blog post).
• other-languages: Example of using the python client to run an evaluation function in a different language.

If you have any questions, comments, or concerns please email us at contact@sigopt.com