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  • March 2022 (Revised July 2022)
  • Module Note

Prediction & Machine Learning

By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
  • Format:Print
  • | Language:English
  • | Pages:44
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Abstract

This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional statistical modeling and machine learning. Next, the note covers two models used for classification, logistic regression and decision trees. After introducing these two models, the note explains how train, validation, and holdout sets (and k-fold cross validation) are used to tune and evaluate different models. Finally, the note concludes with a discussion of different performance metrics (ROC cruves, confusion matrices, log loss) that are used to evaluate classification models.

Keywords

Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation

Citation

Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Module Note 622-101, March 2022. (Revised July 2022.)

About The Authors

Iavor I. Bojinov

Technology and Operations Management
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Michael I. Parzen

Technology and Operations Management
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Related Work

    • March 2022 (Revised July 2022)
    • Faculty Research

    Prediction & Machine Learning

    By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
Related Work
  • Prediction & Machine Learning By: Iavor I. Bojinov, Michael Parzen and Paul Hamilton
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