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Table Of Contents

  • 1. Getting Started
  • 2. Photometric Data
  • 3. Data Preprocessing
  • 4. Performance Measures
  • 5. Active Learning
  • 6. Visualisations
  • 7. API Reference

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Documentation of mclearn¶

Multiclass Active Learning Algorithms with Application in Astronomy.

Author:Alasdair Tran
License:This package is distributed under a a 3-clause (“Simplified” or “New”) BSD license.
Source:https://github.com/alasdairtran/mclearn

Contents¶

  • 1. Getting Started
    • 1.1. Introduction
    • 1.2. Installation
    • 1.3. Example Notebooks
  • 2. Photometric Data
    • 2.1. Dust Extinction
  • 3. Data Preprocessing
    • 3.1. Normalisation
    • 3.2. Balanced Train-Test Split
  • 4. Performance Measures
    • 4.1. Naive Accuracy
    • 4.2. Balanced Accuracy
    • 4.3. Recall
    • 4.4. Precision
  • 5. Active Learning
    • 5.1. Main Active Learning Routine
    • 5.2. Heuristics
  • 6. Visualisations
    • 6.1. Learning Curves
    • 6.2. Mollweide Projection
    • 6.3. Miscellaneous Visualisations
  • 7. API Reference
    • 7.1. Classifiers
    • 7.2. Active Learner
    • 7.3. Active Learning Heuristics
    • 7.4. Performance Measures
    • 7.5. Photometric Data
    • 7.6. Data Preprocessing
    • 7.7. Visualisations

Indices and tables¶

  • Index
  • Module Index
  • Search Page

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