In machine learning, bias refers to a systematic error introduced by an algorithm that causes it to consistently make certain predictions. Bias can arise from assumptions made by the model to simplify the learning process.
Use Cases
Model Building
Understanding how bias affects the outcomes of data analysis.
Data Analysis
Understanding how bias affects the outcomes of data analysis.
Importance
Fairness
Ensuring models do not favor certain groups or outcomes unfairly.
Accuracy
Improving the accuracy of predictions by addressing bias
Trustworthiness
Building trust in AI systems by making them more reliable and unbiased.
Analogies
Bias in machine learning is like a crooked ruler. If you measure everything with a crooked ruler, all your measurements will be off in a consistent way. Similarly, a biased model consistently makes inaccurate predictions.
