Cherry picking pa Another form of cherry picking is known as "selection bias," which occurs when the data used to train a model is not representative of the population that the model will be used on. For example, if a model is trained on data from a particular region or demographic group, it may not perform as well when applied to data from a different region or group. To avoid cherry picking, it is important to use best practices in data collection, preprocessing, and analysis. This includes: * Collecting data that is representative of the population of interest * Using random sampling techniques to ensure that the data is unbiased One common form of cherry picking in machine learning is known as "p-hacking" or "data dredging." This occurs when a researcher tests many different hypotheses or models on a dataset, and then selects only the ones that produce statistically significant results. This can lead to false positives, where the researcher concludes that there is a real effect or relationship in the data when in fact there is not. Another form of cherry picking is known as "selection bias," which occurs when the data used to train a model is not representative of the population that the model will be used on. For example, if a model is trained on data from a particular region or demographic group, it may not perform as well when applied to data from a different region or group. To avoid cherry picking, it is important to use best practices in data collection, preprocessing, and analysis. This includes: * Collecting data that is representative of the population of interest In summary, cherry picking is a serious issue in machine learning that can lead to false positives, biased models, and misleading conclusions. To avoid cherry picking, it is important to use best practices in data collection, preprocessing, and analysis, and to be transparent about the methods and assumptions used. By following these guidelines, researchers can help ensure that their machine learning models are accurate, reliable, and trustworthy.
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