Feature scaling is a technique used to standardize the range of independent variables or features in a dataset. Many Machine Learning algorithms work better or converge faster when features are on a similar scale, especially those that use distance calculations, such as K-Nearest Neighbors or Support Vector Machines.<\/p>\n\n\n\n
In datasets, features may have very different ranges. For example, one feature may represent age (0\u2013100) and another feature may represent income (0\u2013100,000). If the features are not scaled, models may give more importance to larger values, which can lead to poor performance.<\/p>\n\n\n\n
Min-Max Scaling transforms features to a fixed range, usually 0 to 1. It is done using the formula:<\/p>\n\n\n\n
This scaling preserves the shape of the original distribution but rescales the values to a uniform range.<\/p>\n\n\n\n Standardization transforms features to have a mean of 0 and a standard deviation of 1. The formula is:<\/p>\n\n\n\n This method is useful when the data has outliers or does not follow a uniform distribution.<\/p>\n\n\n\n Robust Scaling uses the median and interquartile range instead of mean and standard deviation. This method is less sensitive to outliers and works well for data with extreme values.<\/p>\n\n\n\n Feature scaling is an essential preprocessing step in Machine Learning. Properly scaled features ensure that the model treats all variables equally and improves convergence and accuracy. Choosing the right scaling method depends on the type of data and the Machine Learning algorithm being used.<\/p>\n\n\nX_scaled = (X - X_min) \/ (X_max - X_min)<\/code><\/p>\n\n\n\nStandardization (Z-Score Normalization)<\/h3>\n\n\n\n
X_standard = (X - mean) \/ standard deviation<\/code><\/p>\n\n\n\nRobust Scaling<\/h3>\n\n\n\n
When to Use Feature Scaling<\/h2>\n\n\n\n
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Conclusion<\/h2>\n\n\n\n