What is confirmation bias

Confirmation bias in Machine Learning is a phenomenon in which data scientists and machine learning engineers allow their preconceived notions to influence their decision-making process. Confirmation bias occurs when a data scientist or machine learning engineer looks for, or interprets, data in a way that confirms their existing beliefs or hypotheses.

Confirmation bias can be especially dangerous in machine learning because it can lead to incorrect assumptions and poor decisions. For example, a data scientist might interpret a dataset in a way that confirms their hypothesis, without considering alternative interpretations of the data. This can lead to incorrect assumptions and models that are not accurate.

Confirmation bias can also lead to over-fitting, which is when a machine learning model is too closely tied to the data it was trained on. Over-fitting can lead to inaccurate predictions when the model is applied to new data.

In order to avoid confirmation bias, data scientists and machine learning engineers should strive to be as objective as possible when interpreting data and constructing models. They should also be open to alternative interpretations of the data and consider different models and approaches.

Finally, data scientists and machine learning engineers should use techniques such as cross-validation and bootstrapping to ensure that their models are not over-fitted and are generalizable to new data. Cross-validation is a technique in which the data is divided into training and testing sets, and the model is evaluated on the testing set to assess its accuracy. Bootstrapping is a technique in which new data is generated from the existing data set, and the model is evaluated on the new data to assess its accuracy.

By using techniques such as cross-validation and bootstrapping, data scientists and machine learning engineers can avoid confirmation bias and create more accurate models.