Log transformation in machine learning

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  • When should you log transform data

    • Log transformation!

    When to Log Transform Data?

    Log transformation is a common technique used in data analysis to improve the distribution of data, making it more suitable for statistical modeling and analysis.

    But when should you log transform your data?

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  • Why log transform data for regression

    • In this article, we’ll explore the reasons why you might need to log transform your data and provide guidelines on when to do so.

    What is Log Transformation?

    Log transformation, also known as logarithmic transformation, is a process of converting data from its original scale to a new scale using the logarithm function.

    The most common type of log transformation is the natural logarithm (ln), but other types such as log10, log2, and logarithm to the base 3 are also used. Log transformation can be used to:

    • Stabilize variance: When the variance of a dataset is not constant, log transformation can help to stabilize it, making it easier to model and analyze.
    • Normalize data: Log transformation can help to normalize data by reducing the effect of extreme values, making it easier to compare and analyze different groups or categories.
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