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--- |
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library_name: tf-keras |
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tags: |
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- structured-data |
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- tabular-data |
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- classification |
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--- |
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# Tensorflow Keras Implementation of Structured data classification from scratch |
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This repo contains models and notebook for [Structured data classification from scratch](https://keras.io/examples/structured_data/structured_data_classification_from_scratch/). |
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This example demonstrates how to do structured data classification, starting from a raw CSV file. Our data includes both numerical and categorical features. We will use Keras preprocessing layers to normalize the numerical features and vectorize the categorical ones. |
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Full credits to [François Chollet](https://twitter.com/fchollet), creator of Keras! |
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## Model description |
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The model is a very simple MLP with only one hidden layer. |
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This example showcases how to perform preprocessing of common tabular data *inside* a Keras model. |
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It uses tensorflow.keras.layers.{IntegerLookup, Normalization, StringLookup} to process numerical and categorical (integer or string) features. |
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## Intended uses & limitations |
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This tool does not provide medical advice It is intended for informational purposes only. It is not a substitute for professional medical advice, diagnosis or treatment. |
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## Training and evaluation data |
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[Our dataset](https://archive.ics.uci.edu/ml/datasets/heart+Disease) is provided by the Cleveland Clinic Foundation for Heart Disease. It's a CSV file with 303 rows. Each row contains information about a patient (a sample), and each column describes an attribute of the patient (a feature). We use the features to predict whether a patient has a heart disease (binary classification). |
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The model is trained on 80% of data and evaluated on remaining 20%. |
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## Training procedure |
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Training proceeds for 50 epochs with default Adam optimizer on binary crossentropy. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
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- training_precision: float32 |
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## Training Metrics |
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Training achieves >83% accuracy on the held-out validation data. |
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Check TensorBoard "Metrics" tab above for details. |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
