Sample Bias In Machine Learning

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Sample Bias In Machine Learning. Delete observations that contain missing values because these represent less than 5% of the data. For example, given a sample of 15 items, you want to test if the sample mean is the same as a hypothesized mean (population).

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For example, given a sample of 15 items, you want to test if the sample mean is the same as a hypothesized mean (population). What should the ml engineer do to minimize bias due to missing values? Delete observations that contain missing values because these represent less than 5% of the data.

For example, given a sample of 15 items, you want to test if the sample mean is the same as a hypothesized mean (population).

It is typically implemented on small samples. Delete observations that contain missing values because these represent less than 5% of the data. It is typically implemented on small samples. For example, given a sample of 15 items, you want to test if the sample mean is the same as a hypothesized mean (population).

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