What is Bias in Machine Learning?

priyankaraul

I am new to ML and was curious about the term “BIAS”. Can you explain it.

Shriyansh

In the field of machine learning, bias refers to the tendency for a model to consistently make incorrect assumptions or predictions based on incomplete or erroneous data. This can be a serious problem when developing algorithms for use in practical applications, as it can lead to significant errors in decision-making or predictions. Bias can arise from several sources, including the selection of training data, the choice of features used in the model, or the assumptions made about the underlying distribution of the data.

Researchers and engineers are constantly working to develop techniques for mitigating bias in machine learning, such as carefully selecting training data that is representative of the population, using feature selection methods that minimize the impact of irrelevant or skewed data, and developing algorithms that are robust to deviations from the expected distribution of the data. Ultimately, the goal is to create machine learning models that are both accurate and fair, ensuring that all individuals and groups are treated equally and with dignity.