Coverage bias

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See also: Machine learning terms

Coverage Bias in Machine Learning

Coverage bias, also referred to as sampling bias, is a form of bias that occurs in machine learning when the data used to train a model does not accurately represent the target population or the problem space. This leads to models that may perform well on the training data, but poorly on the general population, ultimately resulting in biased predictions or decisions. The primary cause of coverage bias is the non-random selection of samples from the population.

Causes of Coverage Bias

There are several factors that can contribute to coverage bias in machine learning:

  • Limited Data Availability: In some cases, only a subset of the population data may be available for training, which may not accurately represent the entire population. This can lead to a model that is biased towards the available data.
  • Data Collection Methods: The method used to collect data can introduce bias if it systematically favors certain types of data points. For example, an online survey may not be representative of the general population if it only reaches individuals who use the internet frequently.
  • Temporal Changes: Changes in the target population over time can cause the training data to become outdated, resulting in a model that no longer accurately represents the current population.
  • Geographical Bias: Data collected from specific geographical areas may not be representative of the broader population, leading to biased models when applied to other regions.

Mitigating Coverage Bias

There are several strategies that can be employed to mitigate the effects of coverage bias in machine learning models:

  • Stratified Sampling: By dividing the target population into homogeneous groups or strata, and sampling proportionally from each stratum, a more representative sample can be obtained. This can help reduce coverage bias introduced by non-random sampling.
  • Data Augmentation: Techniques like data augmentation can be used to artificially expand the training dataset, potentially reducing the impact of coverage bias. However, care must be taken not to introduce new biases through the augmentation process.
  • Updating Training Data: Regularly updating the training data to reflect changes in the target population can help maintain a model's accuracy and minimize the impact of coverage bias.
  • Domain Adaptation: In cases where the training data is known to be biased, domain adaptation techniques can be employed to adjust the model to better represent the target population.

Explain Like I'm 5 (ELI5)

Imagine you want to make a cake, and your friend gives you a recipe. You trust this recipe because your friend is an excellent baker. However, your friend only had a few ingredients when they created the recipe, so the cake doesn't taste very good when you try to make it with your full set of ingredients. This is similar to coverage bias in machine learning. If the data used to train a model (the recipe) doesn't represent the whole population (all possible ingredients), then the model's predictions or decisions (the cake) might not be very good. To fix this, you can try to collect more data or use different techniques to make the model better represent the whole population.