Question

What is skewness in machine learning?

Answer 1

Answer:

Skewness is a numerical indicator of how far a data sample deviates from the normal distribution. The data is visually represented as a bell-shaped curve with equal mean (average) and mode (highest value in the data set) in a normal distribution.

Step-by-step explanation:

• If the data distribution's mean is smaller than the mode, there will be more graphed points to the left of the mode than to the right, resulting in a "negative skew."
• If the data distribution's mean is greater than the mode, there will be more graphed points to the right of the mode than to the left, resulting in a "negative skew."
• Any kind of skewness is undesirable in most models because it leads to overly wide variation in estimates. Other models, such as unbiased estimators or Gaussian models, require unbiased estimators or Gaussian models to work well.
• In any scenario, the objective is to use "transformations," such as calculating the inverse, logarithm, or square roots of all the datapoints, to decrease skewness and reach as near to a normal distribution as feasible (normalising data).