Negatively Skewed

In data science, negatively skewed refers to a distribution of data that is skewed to the left, meaning that the tail of the distribution is longer on the left side than on the right side. This indicates that there are more extreme values on the left side of the distribution, and the mean is typically less than the median. Negatively skewed data can occur in various contexts, such as income distribution, where a few individuals have very high incomes, and most people have lower incomes. In machine learning, negatively skewed data can pose challenges for model training, as the model may be biased towards the majority class. Therefore, it is important to preprocess the data and balance the classes before training the model.
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