metadata

language:
  - en
thumbnail: null
tags:
  - classification
  - gradient boosted trees
  - keras
  - TensorFlow
libraries: TensorBoard
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
pipeline_tag: structured-data-classification

Classification with TensorFlow Decision Forests

Using TensorFlow Decision Forests for structured data classification

##### This example uses Gradient Boosted Trees model in binary classification of structured data, and covers the following scenarios:

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.

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.

Author: Khalid Salama
Adapted implementation: Tannia Dubon