Скачать или смотреть bias and fairness in machine learning part 2 building a baseline

Get Free GPT4.1 from https://codegive.com/6e3d9ba
Okay, let's dive into the second part of our deep dive into bias and fairness in machine learning. This section focuses on building a baseline model, which is a crucial step for understanding and mitigating bias. A baseline provides a reference point against which to measure the impact of fairness interventions.

*Part 2: Building a Baseline Model & Initial Assessment*

*1. Why Build a Baseline?*

A baseline model serves several crucial purposes:

*Performance Benchmark:* It establishes a starting point for model performance (accuracy, precision, recall, F1-score, etc.). You'll want any fairness-aware interventions you apply to improve upon the baseline, not degrade performance.
*Bias Identification:* Analyzing the baseline's predictions across different demographic groups (e.g., by race, gender, age) can reveal initial signs of bias. Are there disparities in performance metrics between groups? This helps you pinpoint which groups are disproportionately affected.
*Fairness Metric Evaluation:* A baseline allows you to calculate baseline fairness metrics (e.g., disparate impact, equal opportunity difference). This serves as the starting point for measuring the impact of any fairness intervention you employ.
*Comparison Point:* Later, when you implement fairness-aware techniques, you'll compare the performance and fairness metrics of the modified model to the baseline. This shows whether your interventions are effective and worth the complexity.

*2. Selecting a Baseline Model*

The choice of baseline model depends on several factors:

*Simplicity:* For initial analysis, start with a simple, interpretable model. This makes it easier to understand why the model is making certain predictions and where potential biases might stem from. Examples include:
*Logistic Regression:* For binary classification problems. Provides probabilities and is easily interpretable.
*Decision Trees:* Easy to visualize and unde ...

#bytecode #bytecode #bytecode