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In Machine Learning Mean Vs Median? When to use which?

The mean and median are both measures of central tendency, which are used to describe the middle or typical value in a set of numerical data. The mean is calculated by adding up all of the values in the data set and dividing by the number of values, while the median is the middle value in a sorted data set.

The mean is generally considered to be a more accurate measure of central tendency than the median for a large data set, because it takes into account all of the values in the data set. However, the median is often preferred in cases where the data set contains outliers, or extremely high or low values, because it is less affected by these extreme values.

In a data set with a symmetrical distribution, the mean and median will be the same. In a data set with a skewed distribution, the mean and median will be different, with the mean being pulled in the direction of the skewness and the median remaining at the center of the data.

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