Welcome to MLT’s documentation!

MLT is the MachineLearning Testbench that has been created as part of a Masters’ Thesis written at the University of applied Sciences and Arts, Dortmund.

Contents:

• Requirements
• Design Principles
• Getting Started
  • Tensorflow-GPU
  • Minimal Working Example
• Tests
  • Test Structure
  • Adding new tests
• MLT Modules
  • MLT.datasets
    • CICIDS2017
    • NSL_KDD
  • MLT.implementations
    • Autoencoder
    • HBOS
    • IsolationForest
    • LSTM_2_Multiclass
    • RandomForest
    • XGBoost
  • MLT.metrics
    • Base Metrics
    • Metrics related to Confusion Matrices
    • Feature Distribution Metrics
    • ROC and AUC Metrics
  • MLT.testrunners
    • Benchmark
    • K-Fold Crossvalidation
  • MLT.tools
    • PredictionEntry
    • Dataset Tools
    • Keras Helper
    • Pyod Helper
    • Scikit Helper
    • Email Tools
    • Result Helper
    • Uncategorized Tools

Indices and tables

• Index
• Module Index
• Search Page