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  • All HBS Web  (11)
    • Faculty Publications  (5)

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    • All HBS Web  (11)
      • Faculty Publications  (5)

      Confusion MatrixRemove Confusion Matrix →

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      • January 2025
      • Technical Note

      AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix

      By: Tsedal Neeley and Tim Englehart
      This technical note introduces the confusion matrix as a foundational tool in artificial intelligence (AI) and large language models (LLMs) for assessing the performance of classification models, focusing on their reliability for decision-making. A confusion matrix... View Details
      Keywords: Reliability; Confusion Matrix; AI and Machine Learning; Decision Making; Measurement and Metrics; Performance
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      Neeley, Tsedal, and Tim Englehart. "AI vs Human: Analyzing Acceptable Error Rates Using the Confusion Matrix." Harvard Business School Technical Note 425-049, January 2025.
      • February 2021
      • Tutorial

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael Toffel and Natalie Epstein
      This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and... View Details
      Keywords: Statistics; Experiments; Forecasting and Prediction; Performance Evaluation; AI and Machine Learning
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      Toffel, Michael, and Natalie Epstein. Assessing Prediction Accuracy of Machine Learning Models. Harvard Business School Tutorial 621-706, February 2021. (Click here to access this tutorial.)
      • January 2021
      • Article

      Machine Learning for Pattern Discovery in Management Research

      By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
      Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details
      Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning
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      Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.
      • August 2018 (Revised September 2018)
      • Supplement

      Predicting Purchasing Behavior at PriceMart (B)

      By: Srikant M. Datar and Caitlin N. Bowler
      Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
      Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
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      Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)
      • August 2018 (Revised September 2018)
      • Supplement

      LendingClub (C): Gradient Boosting & Payoff Matrix

      By: Srikant M. Datar and Caitlin N. Bowler
      This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default... View Details
      Keywords: Data Analytics; Data Science; Investment; Financing and Loans; Analytics and Data Science; Analysis; Forecasting and Prediction
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      Datar, Srikant M., and Caitlin N. Bowler. "LendingClub (C): Gradient Boosting & Payoff Matrix." Harvard Business School Supplement 119-022, August 2018. (Revised September 2018.)
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