Bias and Variances

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• Evaluation
  • Evaluation - Measures
    • Accuracy
    • Precision & Recall (Sensitivity)
    • Specificity
  • Benchmarks
  • Explainable Artificial Intelligence (XAI)
  • Train, Validate, and Test
• A Basic Recipe for Machine Learning | Hafidz Zulkifli
• Inductive Bias | Wikipedia
• Overfitting Challenge
• This is how AI bias really happens—and why it’s so hard to fix | Karen Hao
• Top 6 most common statistical errors made by data scientists | Richa Bhatia - Analytics India
• Data Science Concepts Explained to a Five-year-old | Megan Dibble - Toward Data Science
• Machine learning and bias | IBM AI Fairness 360 Open Source Toolkit extensible open source toolkit can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle. Containing over 70 fairness metrics and 10 state-of-the-art bias mitigation algorithms
• Was your Uber, Lyft fare high because of algorithm bias? | Coral Murphy - USA TODAY
• Attacking discrimination with smarter machine learning | Martin Wattenberg, Fernanda Viegas, and Moritz Hardt

• Exploring and Visualizing an Open Global Dataset | Reena Jana and Josh Lovejoy