The importance of privacy in Machine Learning is growing and several open source libraries have been developed by Google, IBM. Let’s check out a multi-text classification example.
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Google AI Research
Multi-class Classification with Linear and Boosted Trees Classifier Introduction Machine learning models include the step of preprocessing or feature engineering before the data is actually trainable. Feature Engineering includes normalizing and scaling data, encoding categorical values as numerical values, forming vocabularies, and binning of continuous numerical values. Distributed frameworks like Google Cloud Dataflow or Apache Spark are often well known for applying large scale data preprocessing. To remove the inconsistency between training and serving ML models from […]
April 27, 2020
Modeling Bank’s Churn Rate with AdaNet: A Scalable, Flexible Auto-Ensemble Learning Framework
Logistic Regression with Google’s AdaNet- Auto Learning Ensemble Framework Motivation AdaNet provides a framework that could automatically produce a high-quality model given an arbitrary set of features and a model search space. In addition, it builds ensembles from productionized TensorFlow models to: Reduce churn, reuse domain knowledge, and conform with business and explainability requirements. The framework is capable of handling datasets containing thousands to billions of examples, in a distributed environment. Several open-source AutoML frameworks, […]
