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README.md
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# TensorFlow Decision Forests for structured data classification
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Use TF's Gradient Boosted Trees model in binary classification of structured data
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* Build a decision forests model by specifying the input feature usage.
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* Implement a custom Binary Target encoder as a Keras Preprocessing layer to encode the categorical features with respect to their target value co-occurrences, and then use the encoded features to build a decision forests model
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<br />
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Author: Khalid Salama
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Adapted implementation: Tannia Dubon
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---
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# TensorFlow Decision Forests for structured data classification
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Use TF's Gradient Boosted Trees model in binary classification of structured data <br />
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+
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* Build a decision forests model by specifying the input feature usage.
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* Implement a custom Binary Target encoder as a Keras Preprocessing layer to encode the categorical features with respect to their target value co-occurrences, and then use the encoded features to build a decision forests model.<br />
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The example uses Tensorflow 7.0 or higher. It uses the US Census Income Dataset containing approximately 300k instances with 41 numerical and categorical variables. This is a binary classification problem to determine whether a person makes over 50k a year.<br />
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Author: Khalid Salama
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Adapted implementation: Tannia Dubon
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