In ad networks, standardizing data is an important prerequisite for accurate targeting. Data standardization is mainly to unify the scale and numerical transformation of data to eliminate the differences between different data sources and improve the comparability and reliability of data. This article will describe the data normalization methods commonly used in ad networks.
1. Min-Max standardization.
Min-Max normalization is a commonly used method of data normalization that maps the values of data to a range of 0-1. This method unifies the values of the data on a scale by subtracting the minimum value from the value of each data and then dividing it by the difference between the maximum and minimum values. The advantage of min-max normalization is the ability to preserve the original distribution and relative relationships of the data, which is applicable to most types of data.
2. Standardization of z-score.
Z-score normalization is a data normalization method based on normal distribution. The method converts the values of the data into a range of the standard normal distribution by calculating the standard deviation between each data value and the mean. The advantage of z-score normalization is that it eliminates correlations between data, which is suitable for situations where differences between different data sets need to be compared.
3. Standardization by decimal point.
Normalization to decimal places is a simple and practical way to normalize data. This method unifies the values of the data on a scale by moving the value of the data to the decimal point of the specified number of digits. For example, move all data to two decimal places, and then zero the decimal places that are less than two decimal places. The advantage of standardizing by decimal place is that it simplifies the presentation of data and facilitates analysis and comparison.
Fourth, normalized treatment.
Normalization is a method of data normalization that maps the values of data to a specific range. This method unifies the values of the data on a scale by dividing the values of the data by the difference between the maximum and minimum values. The advantage of normalization is the ability to map the values of the data to specific ranges, which is useful when the data needs to be scaled.
5. Logarithmic transformation.
Logarithmic transformation is a data normalization method that converts the values of data nonlinearly. This method converts the value of the data nonlinearly by replacing the value of the data with a logarithmic value based on 10. The advantage of logarithmic transformation is that it can reduce the variance of the data and make the distribution of the data more uniform.
In summary, the commonly used data normalization methods in ad networks include min-max normalization, z-score normalization, normalization by decimal place, normalization, and logarithmic transformation. Different methods are suitable for different situations and data types, and it is necessary to select the appropriate method for data standardization according to actual needs. At the same time, it is also necessary to establish a sound data quality monitoring mechanism to detect and deal with data anomalies in a timely manner to ensure the accuracy and completeness of data.