Скачать или смотреть Addressing Bias and Improving Model Performance: A Technical Overview

Bias in machine learning models can have significant consequences, leading to unfair outcomes and perpetuating existing social inequalities.
To develop reliable and fair models, it is essential to address bias and improve model performance.

Model bias can arise from various sources, including data quality, sampling methods, and algorithmic design.

Several techniques can be employed to address bias and improve model performance, including data preprocessing, feature selection, and regularization methods.

To further reinforce your understanding of this topic, consider exploring the following resources:

Additional *Resources*
Research papers on bias detection and correction in machine learning
Documentation on fair machine learning frameworks and libraries
Online courses on machine learning fairness and ethics

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