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    • Faculty Publications  (4)

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    • All HBS Web  (7)
      • Faculty Publications  (4)

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      Machine Learning for Pattern Discovery in Management Research
      Assessing Prediction Accuracy of Machine Learning Models
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      • 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: Machine Learning; Statistics; Experiments; Forecasting and Prediction; Performance Evaluation
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      Toffel, Michael, and Natalie Epstein. Assessing Prediction Accuracy of Machine Learning Models. Harvard Business School Tutorial 621-706, February 2021.
      • August 2020 (Revised September 2020)
      • Technical Note

      Assessing Prediction Accuracy of Machine Learning Models

      By: Michael W. Toffel, Natalie Epstein, Kris Ferreira and Yael Grushka-Cockayne
      The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...  View Details
      Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Data and Data Sets; Analysis; Mathematical Methods
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      Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
      • 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; Data and Data Sets; 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; Data and Data Sets; 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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      Machine Learning for Pattern Discovery in Management Research
      Assessing Prediction Accuracy of Machine Learning Models
      → Search All HBS Web
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