Data Preprocessing Questions Medium
Data standardization techniques are used in data preprocessing to transform data into a common scale or format, ensuring that the data is consistent and comparable. There are several different data standardization techniques commonly used, including:
1. Z-score normalization: This technique standardizes the data by subtracting the mean and dividing by the standard deviation. It transforms the data to have a mean of 0 and a standard deviation of 1.
2. Min-max scaling: This technique scales the data to a specific range, typically between 0 and 1. It subtracts the minimum value from each data point and divides by the range (maximum value minus minimum value).
3. Decimal scaling: In this technique, the data is divided by a power of 10, such that the absolute maximum value becomes less than 1. This ensures that all data points are within the same order of magnitude.
4. Log transformation: This technique is used when the data has a skewed distribution. It applies a logarithmic function to the data, which compresses the larger values and expands the smaller values, making the distribution more symmetrical.
5. Unit vector scaling: Also known as normalization, this technique scales the data to have a length of 1. It divides each data point by the Euclidean norm of the data vector.
6. Robust scaling: This technique is similar to min-max scaling, but it uses the median and interquartile range instead of the minimum and maximum values. It is more robust to outliers and extreme values.
These data standardization techniques are applied based on the specific characteristics and requirements of the dataset and the machine learning algorithm being used. The choice of technique depends on the nature of the data and the desired outcome of the preprocessing step.