Contents
What does log normalization mean?
Making sense of log events is called normalization. Normalization translates log events of any form into a LogPoint vocabulary or representation. The vocabulary is called a taxonomy. So, to put it very compactly normalization is the process of mapping log events into a taxonomy.
What is the difference between scaling and normalization?
Normalization adjusts the values of your numeric data to a common scale without changing the range whereas scaling shrinks or stretches the data to fit within a specific range. Scaling is useful when you want to compare two different variables on equal grounds.
When does log transformation lead to a normal distribution?
Log transformation leads to a normal distribution only for log-normal distributions. Not all distributions are log-normal, meaning they will not become normal after the log transformation. Not all distributions are log-normal, meaning they will not become normal after the log transformation.
Why do you need to log transform your data?
The reason for log transforming your data is not to deal with skewness or to get closer to a normal distribution; that’s rarely what we care about. Validity, additivity, and linearity are typically much more important. The reason for log transformation is in many settings it should make additive and linear models make more sense.
Which is an example of a log transformation?
For example, a treatment that increases prices by 2%, rather than a treatment that increases prices by $20. The log transformation is particularly relevant when the data vary a lot on the relative scale. Increasing prices by 2% has a much different dollar effect for a $10 item than a $1000 item.
When to use original scale or log transform?
From page 65: If a variable has a narrow dynamic range (that is, if the ratio between the high and low values is close to 1), then it will not make much of a difference in fit if the regression is on the logarithmic or the original scale. . . . In such a situation, it might seem to make sense to stay on the original scale for reasons of simplicity.