readme: ---
  thumbnail:
  
  tags:
  - tabular-classification
  - keras
  - tensorflow
  
  library_name: keras
  
  
  license: apache-2.0
  
  metrics:
  - accuracy
  
  model-index:
  - name: TF_Decision_Trees
    results:
    - task: 
        type: structured-data-classification
      dataset:
        type: census
        name: Census-Income Data Set
      metrics:
      - type: accuracy
        value: 96.57
      - type: validation loss
        value: 0.227394    
   
  ---
  
  # TensorFlow's Gradient Boosted Trees Model for structured data classification
  
  Use TF's Gradient Boosted Trees model in binary classification of structured data <br />
  
  * Build a decision forests model by specifying the input feature usage.
  * 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 />
   
  The model is implemented using Tensorflow 7.0 or higher. The US Census Income Dataset containing approximately 300k instances with 41 numerical and categorical variables was used to train it. This is a binary classification problem to determine whether a person makes over 50k a year.<br /> 
  
  Author: Khalid Salama   
  Adapted implementation: Tannia Dubon
  Find the colab notebook at https://github.com/tdubon/TF-GB-Forest/blob/c0cf4c7e3e29d819b996cfe4eecc1f2728115e52/TFDecisionTrees_Final.ipynb
  