{"id":1525,"date":"2022-02-02T20:07:54","date_gmt":"2022-02-02T20:07:54","guid":{"rendered":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/02\/balance-your-data-for-machine-learning-with-amazon-sagemaker-data-wrangler\/"},"modified":"2022-02-02T20:07:54","modified_gmt":"2022-02-02T20:07:54","slug":"balance-your-data-for-machine-learning-with-amazon-sagemaker-data-wrangler","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2022\/02\/02\/balance-your-data-for-machine-learning-with-amazon-sagemaker-data-wrangler\/","title":{"rendered":"Balance your data for machine learning with Amazon SageMaker Data Wrangler"},"content":{"rendered":"
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Amazon SageMaker Data Wrangler<\/a> is a new capability of Amazon SageMaker<\/a> that makes it faster for data scientists and engineers to prepare data for machine learning (ML) applications by using a visual interface. It contains over 300 built-in data transformations so you can quickly normalize, transform, and combine features without having to write any code.<\/p>\n

Today, we\u2019re excited to announce new transformations that allow you to balance your datasets easily and effectively for ML model training. We demonstrate how these transformations work in this post.<\/p>\n

New balancing operators<\/h2>\n

The newly announced balancing operators are grouped under the Balance data<\/strong> transform type in the ADD TRANFORM<\/strong> pane.<\/p>\n

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Currently, the transform operators support only binary classification problems. In binary classification problems, the classifier is tasked with classifying each sample to one of two classes. When the number of samples in the majority class (bigger) is considerably larger than the number of samples in the minority (smaller) class, the dataset is considered imbalanced. This skew is challenging for ML algorithms and classifiers because the training process tends to be biased towards the majority class.<\/p>\n

Balancing schemes, which augment the data to be more balanced before training the classifier, were proposed to address this challenge. The simplest balancing methods are either oversampling the minority class by duplicating minority samples or undersampling the majority class by removing majority samples. The idea of adding synthetic minority samples to tabular data was first proposed in the Synthetic Minority Oversampling Technique (SMOTE), where synthetic minority samples are created by interpolating pairs of the original minority points. SMOTE and other balancing schemes were extensively studied empirically and shown to improve prediction performance in various scenarios, as per the publication To SMOTE, or not to SMOTE<\/a>.<\/p>\n

Data Wrangler now supports the following balancing operators as part of the Balance data<\/strong> transform:<\/p>\n