Interpretable machine learning applications: Part 5
Completed by CLIFFORD YEE
November 4, 2024
1 hours (approximately)
CLIFFORD YEE's account is verified. Coursera certifies their successful completion of Interpretable machine learning applications: Part 5
What you will learn
 Be acquainted with the basics of the Aequitas Tool as a tool to measure and detect bias in the outcome of a machine learning prediction model.
Learn more about a real world case study, i.e., predictions of recidivism (COMPAS dataset), and how the prediction model may have been biased.
Learn a technique, which is largely based on statistical descriptors, for measuring bias and fairness for Machine Learning (ML) prediction models.
Skills you will gain
- Category: Data Ethics
- Category: Economics, Policy, and Social Studies
- Category: Histogram
- Category: Descriptive Statistics
- Category: Data Visualization
- Category: Development Environment
- Category: Predictive Modeling
- Category: Machine Learning
- Category: Statistical Methods
- Category: Exploratory Data Analysis
- Category: Model Evaluation
- Category: Predictive Analytics
