ndtands/TabularDataProblem
Classification in TabularDataset
This project helps data scientists and machine learning engineers build more accurate classification models from structured datasets. It takes raw tabular data, which often includes a mix of numerical and categorical features, and processes it to predict a specific outcome, such as whether a customer will churn or a loan applicant is high-risk. The output is a highly optimized classification model, along with performance metrics to evaluate its effectiveness.
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Use this if you are a data scientist or machine learning engineer looking to apply advanced deep learning techniques, specifically Transformer architectures, to improve the accuracy of classification tasks on complex tabular datasets.
Not ideal if you need a simple, highly interpretable model, or if your dataset is very small and doesn't warrant the complexity of deep learning transformers.
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Last pushed
Jan 26, 2023
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