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22 行
827 B
Plaintext
22 行
827 B
Plaintext
I have a tabular dataset from UCI Adult Census Income with the following columns:
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- age (number)
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- workclass (category)
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- education (category, ordered from preschool through doctorate)
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- education-num (number, 1-16)
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- marital-status (category)
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- occupation (category, 14 unique values)
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- relationship (category)
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- race (category)
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- sex (binary: Male / Female)
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- capital-gain (number, heavily skewed, mostly zero)
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- capital-loss (number, similar to capital-gain)
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- hours-per-week (number, 1-99)
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- native-country (category, high cardinality ~40 classes)
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The target column is "income" (binary: >50K or <=50K).
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The dataset has about 48k rows. Training should be reasonably fast — prefer the
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medium_quality preset. Use the concat combiner with two FC layers. Use AdamW with a
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learning-rate scheduler.
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