cosbidev/NAIM
Official implementation for the paper ``Not Another Imputation Method: A Transformer-based Model for Missing Values in Tabular Datasets´´
When performing machine learning tasks on tabular datasets, it's common to encounter missing values which complicate model training. This tool helps machine learning engineers and data scientists by providing a robust way to handle missing data directly within a PyTorch-based model, without needing to pre-process or 'fill in' the missing data. You provide your tabular dataset with missing values, and it outputs a trained model and its performance metrics on the dataset.
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Use this if you are a machine learning engineer or data scientist working with tabular data and want an advanced method to train models directly on datasets with missing values, avoiding traditional imputation steps.
Not ideal if you need a simple, off-the-shelf data cleaning tool or if you are not comfortable working with Python, PyTorch, and configuration files for model training.
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Python
License
MIT
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Last pushed
Apr 07, 2025
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