Train, tune and distribute models with generative AI and machine learning capabilities
On March 3, we had an initial discussion with some of the IBM teams who participated in the contest and here are some early remarks and feedback we gathered.
Tools the IBM teams used included Python, Jupyter Notebooks, Watson Studio, AutoAI, Anaconda
Models used included Decision trees, RandomForest, XGBoost, LightGBM, CATboost, SVM, Neural Nets,
Techniques used included under sampling and oversampling, encoding categorical columns, combining multiple models
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