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Interpretable machine learning applications: Part 5 に戻る

Coursera Project Network による Interpretable machine learning applications: Part 5 の受講者のレビューおよびフィードバック


You will be able to use the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model. As a use case, we will be working with the dataset about recidivism, i.e., the likelihood for a former imprisoned person to commit another offence within the first two years, since release from prison. The guided project will be making use of the COMPAS dataset, which already includes predicted as well as actual outcomes. Given also that this technique is largely based on statistical descriptors for measuring bias and fairness, it is very independent from specific Machine Learning (ML) prediction models. In this sense, the project will boost your career not only as a Data Scientists or ML developer, but also as a policy and decision maker....

Interpretable machine learning applications: Part 5: 1 - 2 / 2 レビュー

by Mohamed K



by Pascal U E


Good content, but hard to follow the instructor and do as he does