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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
  • 2. Cost-sensitive and Value-driven models

2. Cost-sensitive and Value-driven models#

  • 2.1. Cost-Sensitive Logistic Regression (CSLogit)
    • 2.1.1. Regularization
    • 2.1.2. Cost Matrix
    • 2.1.3. Optimization
    • 2.1.4. References
  • 2.2. Cost-Sensitive Gradient Boosting (CSBoost & B2Boost)
    • 2.2.1. CSBoost
    • 2.2.2. Cost Matrix
    • 2.2.3. B2Boost
    • 2.2.4. References
  • 2.3. Boosting Algorithms Custom Cost Functions
  • 2.4. Robust Cost-Sensitive Classification (RobustCS)
    • 2.4.1. Usage
    • 2.4.2. References
  • 2.5. Profit-Driven Logistic Regression (ProfLogit)
    • 2.5.1. Regularization
    • 2.5.2. Optimization
    • 2.5.3. References

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1.2. Define your own cost-sensitive or value metric

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2.1. Cost-Sensitive Logistic Regression (CSLogit)

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