What is bias in data model?

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In data science, there are several types of bias. Pure model bias does not interact with the real world bias. If you’re wondering, bias is a measure of your model’s ability to precisely fit the training data. Take the example of a chemical manufacturer with a model designed to predict when a vat will boil over to cut off the heat source and save the product. This model performed well in testing and training but only predicted the occurrence of boil-overs on Tuesdays. So how do you avoid bias in your data model?

Sample bias

There are many reasons why a data model might have sample bias. The bias can be caused by the way data is collected, the choice of the analyst, or both. The model data may not represent the entire population in the most extreme example. In such cases, it may be helpful to use event-based data sources. Event-based data sources update data automatically over time and can also be useful in developing machine learning models.

In the case of a chemical manufacturer, for example, a model could be trained to predict when a vat will boil over so that it can be shut off quickly to save the product. The model worked well during training and testing. For example, it correctly predicted that boil-over events are more likely to occur on Tuesdays. But if the data were collected on different days of the week, the model would have a more accurate prediction.

Recall bias

Recall bias occurs in many types of studies, but it is most prevalent in case-control and retrospective cohort designs. It happens when subjects report their prior and subsequent outcomes with varying degrees of completeness. Similarly, the Ranch Hand Study, which examined the effects of dioxin on ranchers, shows that pilots remember the appearance of skin rashes better than others. Such a bias can significantly affect estimates of the size of an effect, such as cancer risk.

Recall bias can increase or decrease the strength of an observed association, especially for long-term events such as childhood infections. In a recent study, the authors of a prospective cohort of women studied the effect of different fat types and intake levels on colorectal cancer risk. They found that dietary fat was associated with a lower risk of cancer. This association was greater among cancer patients, whereas cancer survivors were less likely to remember childhood infections.

Group attribution

In data-driven attribution, there are many flavors. For instance, the matching methodology involves identifying two groups of users, which might be called a pseudo-control and pseudo-test. These groups are matched to be similar along a variety of dimensions, such as age, gender, social-economic status, and propensity to buy. Then, you compare the two groups. For example, if a group were more likely to click on a particular ad, the other would be less likely to click or view it.

Another way to apply attribution is through the bathtub model. It is similar to last-click attribution but combines the two types. The first and last contact points are given higher ratings than the rest. The monetary share between the two points is fixed at 40% by default, and the remaining stages of the purchase process share the rest of the conversion value. You need to map the entire customer journey using a data-driven model to get the most out of your attribution efforts. An intelligent algorithm will be able to assess the importance of each touchpoint as a result of the customer journey.

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Selection bias

Selective bias occurs when a study’s sample population does not represent the whole population because data sets are not randomized, they cannot reflect the entire population. Similarly, not randomized data is likely to contain confounding factors that influence results. In some cases, selection bias can overlap with racial bias. Good researchers always look for ways to overcome selection bias. They will attempt to match study and control groups as closely as possible and adjust for any factors that may affect outcomes. In addition, they will discuss the bias in their reports and acknowledge that their results may apply only to specific groups.

The levels of selection bias in a data model are correlated with various statistical measures of fairness and accuracy. For example, with increasing selection bias, precision and F1 scores decrease. Similarly, error rate balance is influenced by factors such as gender, race, or regional preference. For example, a study that predicts heart failure must focus on predominantly white males. However, the age-standardized incidence rates of heart failure differ for women.

Reporting bias

Self-reporting bias is one of the most common problems with observational research. This bias still exists in experimental studies, though. Self-reporting data offer a wider range of responses than survey data. This type of data can be beneficial for obtaining the views and perspectives of subjects. Unfortunately, self-reporting data cannot be relied upon as a reliable indicator of social, cultural, or health outcomes.

One way to identify reporting bias is to examine the type of data used to compile the results. For example, if a company sent out surveys to 500 customers but only collected data from Apple users, the results of the surveys would not accurately reflect the opinions of Samsung customers. The previous scenario presented is an example of overgeneralization. This problem occurs when we assume that a set of data is representative of all others in the same dataset. Furthermore, we tend to stereotype whole groups based on the actions of a small number of individuals, which is called Group Attribution Bias.

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