Z-score standardization (also called Z-score normalization or simply standardization) is a technique used to scale a dataset so that it has a mean of 0 and a standard deviation of 1. It is used to standardize the data and make sure that all the variables are on the same scale.
The process of Z-score standardization involves the following steps:
- Calculate the mean of the dataset.
- Calculate the standard deviation of the dataset.
- Subtract the mean from each value in the dataset.
- Divide the resulting dataset by the standard deviation.
The Z-score standardization formula is as follows:
X_scaled = (X – Mean) / Standard Deviation
Where X is the original value, Mean is the mean of the dataset and Standard Deviation is the standard deviation of the dataset.
Z-score standardization is a widely used technique for standardizing data, it is commonly used when data is non-normally distributed, or when we want to compare data with different scales. However, it does have some limitations. For example, it assumes that the data is normally distributed, so it may not be suitable for data that has a non-normal distribution. Additionally, it can be sensitive to outliers, so it’s important to remove or treat them differently before applying Z-score standardization.
Z-score standardization is a technique used to standardize data, it has some advantages and disadvantages.
- Z-score standardization makes it possible to compare variables with different scales. By standardizing the data, all variables have the same mean and standard deviation.
- Z-score standardization allows the use of models that assume normality. Many machine learning algorithms assume that the data is normally distributed and Z-score standardization can help to achieve this.
- Z-score standardization can be useful for identifying outliers, as any point that is more than 3 standard deviations away from the mean can be considered an outlier.
- Z-score standardization is not affected by the presence of extreme values, unlike Min-Max normalization.
- Z-score standardization assumes that the data is normally distributed, but it may not be suitable for data that has a non-normal distribution.
- Z-score standardization can be sensitive to outliers, so it’s important to remove or treat them differently before applying Z-score standardization.
- Z-score standardization can cause negative values in the data, which can be a problem for some machine learning algorithms.
- Z-score standardization can make it difficult to interpret the values, as they are no longer in their original units.
It’s important to note that Z-score standardization is just one of the many normalization and standardization techniques available and it should be used in conjunction with other visualization and statistical methods to get a better understanding of the data and to choose the appropriate normalization method. Additionally, it’s important to carefully consider the assumptions and limitations of the standardization method before applying it to your data.