A naukri.com initiative
Self-Learning-Java
3w
149
The Importance of Data Normalization in Machine Learning
- Data normalization is a crucial pre-processing technique in machine learning to bring numerical data to a common scale for enhanced algorithm performance.
- Techniques like Min-Max Normalization, Z-score normalization, Robust Scaling, Max Absolute Scaling, and Logarithmic normalization are commonly used for data normalization.
- Min-Max Normalization scales data to a specific range (often 0 to 1) using a simple formula that involves the minimum and maximum values in the dataset.
- Z-score normalization normalizes data to have a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation.
- Robust scaling, similar to Z-score scaling, utilizes the median and interquartile range (IQR) instead of the mean and standard deviation, making it less sensitive to outliers.
- Max Absolute Scaling, implemented by MaxAbsScaler, scales data by dividing each value by the maximum absolute value, resulting in values between -1 and 1.
- Logarithmic normalization involves taking the logarithm of a feature, either natural logarithm or base 10 logarithm, to transform the data.
- Implementation examples using Python and libraries like NumPy and Scikit-learn demonstrate the practical application of these normalization techniques.
Read Full Article
8 Likes
For uninterrupted reading, download the app