AI News, alexandraj777/machine-learning-samples forked from aws-samples/machine-learning-samples
- On Tuesday, June 5, 2018
- By Read More
alexandraj777/machine-learning-samples forked from aws-samples/machine-learning-samples
This sample code builds a hyperparameter optimization pipeline for Amazon Machine Learning using the latest AWS SDK for Python (Boto 3).
If want to use SigOpt to optimize your hyperparameters faster and better than tuning by hand, sign up for a free trial on our website and grab your API token from your user profile.
This script relies on a manually specified list of hyperparameters to tune the hyperparameters of a linear binary classification model.
Many machine learning models have exposed parameters, commonly known as hyperparameters (Amazon ML sometimes calls them training parameters), that you choose values for before model training begins.
You'll notice that learning rate is also a hyperparameter of your model, but Amazon ML is automatically selecting a value for it based on your data, so we can't tune it in this example.
In keeping with best practices for hyperparameter optimization, and to prevent over-fitting of the model, this example actually maximizes the average of k-fold cross validated AUC metrics.
This description skips over the details of how cross validation is performed with Amazon ML, because it is described much better in the README for the K-Fold Cross Validation example that formed the basis for this code sample.
To perform hyperparameter optimization the scripts iteratively choose new values of regularization_type and regularization_amount, evaluates a model with these new hyperparameters for every fold of the data, averages the AUC metrics, and records the performance of the assignments.
Each time the script evaluates a model on a new set of hyperparameters it creates k machine learning models, one for each train datasource, and k evaluations, one for each evaluate datasource, on Amazon ML.
API calls via the python SDK will return quickly so that you can build a datasource, machine learning model, and an evaluation while the datasource is still pending!
- On Tuesday, March 26, 2019
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