https://github.com/radoslawregula/binary-classification-metrics

A model implementing a solution to the binary classification problem along with several accuracy metrics.
https://github.com/radoslawregula/binary-classification-metrics

binary-classification classification jupyter-notebook machine-learning matplotlib pandas python scikit-learn stochastic-gradient-descent

Last synced: about 2 months ago
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A model implementing a solution to the binary classification problem along with several accuracy metrics.

• Host: GitHub
• URL: https://github.com/radoslawregula/binary-classification-metrics
• Owner: radoslawregula
• Created: 2020-02-09T19:15:48.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-09T20:01:38.000Z (almost 5 years ago)
• Last Synced: 2023-10-20T00:14:14.090Z (about 1 year ago)
• Topics: binary-classification, classification, jupyter-notebook, machine-learning, matplotlib, pandas, python, scikit-learn, stochastic-gradient-descent
• Language: Jupyter Notebook
• Size: 67.4 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Binary classification metrics preview 

The notebook constists of an implementation of the stochastic gradient descent classifier for the problem based on a data set owned by

Volker Lohweg (University of Applied Sciences, Ostwestfalen-Lippe) and available under [this link](https://archive.ics.uci.edu/ml/datasets/banknote+authentication).

The task is to distinguish real and forged banknotes based on certain features of their images. Trained model was tested using metrics described in Chapter 3 of 

Aurelien Geron, 'Hands - On Machine Learing with Scikit-Learn and TensorFlow', Helion SA, 2018. 

scikit-learn library's implementations of machine learning methods and metrics were used. Matplotlib library was used for plotting and visualization.