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  • User Guide
  • API Reference
  • Project Information
  • GitHub
  • PyPI

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  • 1. Metrics
    • 1.1. Choosing the right metric
    • 1.2. Define your own cost-sensitive or value metric
  • 2. Cost-sensitive and Value-driven models
    • 2.1. Cost-Sensitive Logistic Regression (CSLogit)
    • 2.2. Cost-Sensitive Gradient Boosting (CSBoost & B2Boost)
    • 2.3. Boosting Algorithms Custom Cost Functions
    • 2.4. Robust Cost-Sensitive Classification (RobustCS)
    • 2.5. Profit-Driven Logistic Regression (ProfLogit)
  • 3. Model-Independent Preprocessing
    • 3.1. Bias Mitigation
    • 3.2. Cost-Proportionate Sampling
  • 4. Cross-Validation with Instance-dependent Costs
  • 5. Datasets
    • 5.1. Bank Telemarketing Upsell Campaign
    • 5.2. Churn in a TV Subscription Company
    • 5.3. Credit Risk Assessment on a Private Label Credit Card Application
    • 5.4. 2011 Kaggle competition Give Me Some Credit
  • User Guide
  • 1. Metrics

1. Metrics#

  • 1.1. Choosing the right metric
    • 1.1.1. Cost Matrix
    • 1.1.2. Example Cost-Benefit Matrix
    • 1.1.3. Instance-dependent Costs
    • 1.1.4. Converting the cost-matrix to metrics
  • 1.2. Define your own cost-sensitive or value metric
    • 1.2.1. Implementing the MPC measure
    • 1.2.2. Implementing the EMPC measure
    • 1.2.3. Implementing expected cost and savings

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1.1. Choosing the right metric

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