Data Preprocessing Questions Medium
Normalization is a crucial step in data preprocessing that aims to transform the data into a standardized format, ensuring fair comparisons and improving the performance of machine learning algorithms. There are several normalization techniques commonly used in data preprocessing, including:
1. Min-Max normalization (also known as feature scaling): This technique rescales the data to a specific range, typically between 0 and 1. It is achieved by subtracting the minimum value of the feature and dividing it by the range (maximum value minus minimum value). This technique is suitable for data that follows a uniform distribution.
2. Z-score normalization (also known as standardization): This technique transforms the data to have a mean of 0 and a standard deviation of 1. It is achieved by subtracting the mean of the feature and dividing it by the standard deviation. Z-score normalization is useful when the data has a Gaussian distribution.
3. Decimal scaling normalization: This technique involves dividing each value by a power of 10, such that the absolute maximum value becomes less than 1. It preserves the relative ordering of the data and is particularly useful when dealing with financial data.
4. Log transformation: This technique applies a logarithmic function to the data, which helps to reduce the impact of outliers and skewness. It is commonly used when the data has a skewed distribution.
5. Unit vector normalization (also known as vector normalization): This technique scales each data point to have a unit norm, meaning that the Euclidean length of the vector becomes 1. It is often used in text mining and natural language processing tasks.
6. Robust normalization: This technique is resistant to outliers and is based on the median and interquartile range. It scales the data by subtracting the median and dividing it by the interquartile range.
These normalization techniques can be applied depending on the characteristics of the data and the requirements of the specific problem at hand. It is important to choose the appropriate technique to ensure the best results in data preprocessing.