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  • 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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  • 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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  • 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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  • 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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  • 16 Apr 2001
  • Research & Ideas

Breaking the Code of Change

reorganized itself around a market by function matrix structure intended to focus on customers. Compensation systems were aligned with culture change objectives. A skill-based pay system was installed in all production facilities to... View Details
Keywords: by Michael Beer & Nitin Nohria
  • 11 May 2009
  • Research & Ideas

The IT Leader’s Hero Quest

the writing of popular film scripts like The Matrix or Star Wars. Character behaviors, actions, and motivations hook the interest of readers at all levels, regardless of their professional experience with IT, while the plot sets up a... View Details
Keywords: by Martha Lagace
  • 04 Dec 2018
  • First Look

New Research and Ideas, December 4, 2018

Gradient Boosting & Payoff Matrix 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... View Details
Keywords: Dina Gerdeman
  • 08 Jan 2019
  • First Look

New Research and Ideas, January 8, 2019

model to the data by regularizing the coefficients of the logistic regression. Wehunt and Morse then compare the models and select the model most effective at correctly classifying households as expecting. Students explore the relationship between the model’s View Details
Keywords: Dina Gerdeman
  • 22 Mar 2011
  • First Look

First Look: March 22

scope. The case only meant for one discussion pasture to review the Hayes-Wheelwright product-process matrix and the impact of variability on line performance. While comparative numbers for the process choices are provided, the hope would... View Details
Keywords: Sean Silverthorne

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