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---
language:
- en
thumbnail:
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"
---
#TensorFlow Decision Forests for structured data classification
<br />
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 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 />
Author: Khalid Salama
Adapted implementation: Tannia Dubon
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