tempse / sklearn-beginners-template

Example of basic data analysis using machine learning in scikit-learn

Scikit-learn Beginner's Template

A basic multivariate analysis of simulated physics data using the machine learning toolkit scikit-learn. The entire analysis example is conveniently presented in form of Jupyter Notebooks.

This project mainly addresses people who are new to machine learning, scikit-learn or Python and should provide them with code implementations of basic analysis steps and plots. It does not necessarily claim to yield optimal analysis results - not even good ones, necessarily - but should rather illustrate some fundamental concepts of machine learning application.

Prerequisites

The provided example makes use of the following Python libraries/frameworks:

• scikit-learn
• numpy
• pandas
• matplotlib
• seaborn

Machine Learning Concepts

The following concepts are covered and implemented:

• Data visualization
  • Variable histograms
  • Scatter matrix
  • Correlation matrix
  • RadViz
• Data preprocessing
  • Standard scaling
• Data split into training, validation and test samples
• Model definition and training with decision trees / random forest
• Overfitting the data and how to prevent it
• Feature importances
• Model evaluation using training and validation sample
  • MVA output distribution
  • Cut efficiencies plot / MVA cut optimization
  • ROC curve
  • Precision-recall curve
  • Confusion matrix
  • Classification report
• Model application to the test sample / classifier performance assessment

Content overview and file description

Notebooks:

• example analysis - random forest.ipynb: Example analysis of simulated physics data using a random forest classifier.
• overtraining demo - random forest.ipynb: Using simulated physics data and a random forest classifier to demonstrate the effects and consequences of overtraining.

Contributing

This project is far from perfect, not least because the author is relatively new to Python and scikit-learn himself.

If you have any suggestions or improvements, feel free to let me know or contribute in any way you like.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• Many thanks to S. Lehner for valuable feedback on first drafts of this project.
• Following PurpleBooth's README style
• All badges made by shields.io