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
#TensorFlow Decision Forests for structured data classification
Use TF's Gradient Boosted Trees model in binary classification of structured data
- 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