Autoencoder Feature Extraction

Results

Accuracy LogisticRegression Simple: 0.8939
Accuracy LogisticRegression With Autoencoder: 0.9242

Author

Cesar Arcos-Gonzalez: cesar99ag@gmail.com

License

MIT License

Introduction

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data. The encoding is validated and refined by attempting to regenerate the input from the encoding. The autoencoder learns a representation for a set of data, typically for dimensionality reduction, by training the network to ignore insignificant data (“noise”).
The encoder can then be used as a data preparation technique to perform feature extraction on raw data that can be used to train a different machine learning model.

Model

Tools

Python 3
Tensorflow 2
Keras
Scikit-learn
Matplotlib

Dataset

sklearn.datasets make_classification

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