Bias
Definition of Bias
Bias is a systematic error in judgment or decision making, resulting in judgments that are not completely impartial. It can refer to either statistical bias or cognitive bias. Statistical bias is the result of an inaccurate estimate of a population parameter, while cognitive bias is the result of incorrect judgments made by individuals.
What is Bias used for?
Bias is an important concept used in data science and machine learning. Bias is the systematic error that arises when a model’s estimated values deviate from the true values that should be obtained. It occurs when an algorithm has learned to make decisions based on incomplete or limited information, creating a pattern of errors that can negatively impact the accuracy of the model.
In data science and machine learning, bias is used to quantify how well the estimated values match up with the expected values. The lower the bias, the more accurate the estimates are for given data set. Bias also helps identify potential issues with algorithms, such as underfitting or overfitting. Underfitting occurs when an algorithm cannot properly capture all important features in a dataset; resulting in inaccurate predictions due to not having enough information or parameters to understand it correctly. Overfitting occurs when an algorithm uses too much complexity and captures too much detail; resulting in predictions being too specific and not generalizable to other datasets. By quantifying bias, researchers can determine which algorithms are performing better than others and identify potential issues with their models.
Additionally, understanding bias is key to evaluating fairness in algorithms. Algorithms trained on biased data can result in models that create unfair outcomes since they don’t properly consider all variables fairly or accurately; this can lead to serious consequences depending on what decisions they are making with those results (such as credit scoring applications). By measuring bias in models, researchers can take steps towards ensuring fairness by balancing out any disparities between different categories of input variables.
