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language: |
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- en |
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thumbnail: |
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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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libraries: TensorBoard |
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license: apache-2.0 |
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metrics: |
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- accuracy |
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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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pipeline_tag: "structured-data-classification" |
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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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##### This example uses Gradient Boosted Trees model in binary classification of structured data, and covers the following scenarios: |
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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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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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Author: Khalid Salama <br /> |
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Adapted implementation: Tannia Dubon |
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