• SPLIT DECISIONS: PRACTICAL MACHINE LEARNING FOR EMPIRICAL LEGAL SCHOLARSHIP

    Author(s):
    James Ming Chen
    Date:
    2020
    Group(s):
    Michigan State Law Review
    Item Type:
    Article
    Permanent URL:
    https://doi.org/10.17613/tqmw-1z75
    Abstract:
    Multivariable regression may be the most prevalent and useful task in social science. Empirical legal studies rely heavily on the ordinary least squares method. Conventional regression methods have attained credibility in court, but by no means do they dictate legal outcomes. Using the iconic Boston housing study as a source of price data, this Article introduces machine-learning regression methods. Although decision trees and forest ensembles lack the overt interpretability of linear regression, these methods reduce the opacity of black-box techniques by scoring the relative importance of dataset features. This Article will also address the theoretical tradeoff between bias and variance, as well as the importance of training, cross-validation, and reserving a holdout dataset for testing.
    Metadata:
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    Published as:
    Journal article     Show details
    Publisher:
    Michigan State University College of Law
    Pub. Date:
    2020
    Journal:
    Michigan State Law Review
    Volume:
    2020
    Issue:
    5
    Page Range:
    1301 - 1360
    ISSN:
    1087-5468
    Status:
    Published
    Last Updated:
    1 year ago
    License:
    Attribution
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