kaushalshetty/SMOTE
Synthetic Minority Over-sampling Technique
When you have a dataset for training a machine learning model where one category is severely underrepresented, this project helps balance the dataset. It takes your existing imbalanced dataset and generates synthetic but realistic new data points for the minority class, resulting in a more balanced dataset for model training. This is useful for data scientists, machine learning engineers, and researchers working with classification problems facing imbalanced data.
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Use this if your machine learning model is performing poorly because you don't have enough examples of a particular outcome or category in your training data.
Not ideal if you need to impute missing values, detect outliers, or perform general data augmentation without a specific focus on minority class oversampling.
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Python
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Last pushed
Mar 27, 2017
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