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AAN@zurich.ibm.com | Published April 26, 2019
Join us for a walkthrough of how to use the Snap ML library to predict credit default using a publicly available dataset. We will first demonstrate how to pre-process the credit data. Then we will show how to use the Snap Machine Learning. Python API to train logistic regression and random forest models and to make predictions. We will compare Snap Machine Learning with scikit-learn in terms of training time and accuracy score. As a hardware infrastructure we will use a POWER9 system with V100 GPUs.
September 16, 2019
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