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- tags:
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- - structured-data-classification
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  library_name: generic
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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- # Structured Data Classification repository template
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-
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- This is a template repository for structured data classification to support generic inference with Hugging Face Hub generic Inference API. There are two required steps
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- 1. Specify the requirements by defining a `requirements.txt` file.
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- 2. Implement the `pipeline.py` `__init__` and `__call__` methods. These methods are called by the Inference API. The `__init__` method should load the model and preload all the elements needed for inference (model, processors, tokenizers, etc.). This is only called once. The `__call__` method performs the actual inference. Make sure to follow the same input/output specifications defined in the template for the pipeline to work.
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-
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- Example repos
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- * https://huggingface.co/osanseviero/wine-quality
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-
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- ## How to start
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- First create a repo in https://hf.co/new.
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- Then clone this template and push it to your repo.
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- ```
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- git clone https://huggingface.co/templates/structured-data-classification
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- cd structured-data-classification
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- git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME
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- git push --force
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- ```
 
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  library_name: generic
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+
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+ language:
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+ - en
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+
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+ thumbnail:
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+
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+ tags:
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+ - classification
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+ - gradient boosted trees
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+ - keras
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+ - TensorFlow
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+
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+ license: apache-2.0
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+ libraries: TensorBoard
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+
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+ metrics:
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+ - accuracy
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+
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+ model-index:
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+ - name: TF_Decision_Trees
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+ results:
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+ - task:
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+ type: structured-data-classification
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+ dataset:
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+ type: census
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+ name: Census-Income Data Set
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+ metrics:
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+ - type: accuracy
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+ value: 96.57
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+
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+ pipeline_tag: "structured-data-classification"
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+
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  ---
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+
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+ # Classification with TensorFlow Decision Forests
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+ #### Using TensorFlow Decision Forests for structured data classification
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+
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+ <br />
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+ ##### This example uses Gradient Boosted Trees model in binary classification of structured data, and covers the following scenarios:
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+
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+ 1. Build a decision forests model by specifying the input feature usage.
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+ 2. 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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+
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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.
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+
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+ Author: Khalid Salama <br />
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+ Adapted implementation: Tannia Dubon