Jellemznormalizáció

Jellemznormalizáció, often translated as feature scaling or normalization, is a data preprocessing technique used in machine learning and statistics. It involves adjusting the range of independent variables or features of data. The goal is to bring all features to a similar scale, preventing features with larger values from dominating the analysis and potentially biasing the model. This is particularly important for algorithms that are sensitive to the scale of input data, such as gradient descent-based algorithms (like linear regression or neural networks) and distance-based algorithms (like k-nearest neighbors or support vector machines).

There are several common methods for feature normalization. Min-Max scaling, also known as rescaling, transforms features to a fixed range, usually between 0 and 1. The formula for Min-Max scaling is: X_scaled = (X - X_min) / (X_max - X_min). Another popular method is standardization, also called Z-score normalization, which rescales features so that they have a mean of 0 and a standard deviation of 1. The formula for standardization is: X_scaled = (X - mean) / standard_deviation.

The choice of normalization technique often depends on the specific algorithm and the nature of the data. For instance, Min-Max scaling is sensitive to outliers, as they can significantly impact the X_min and X_max values. Standardization, on the other hand, is less affected by outliers. Proper feature normalization can lead to faster convergence of training algorithms, improved model performance, and more reliable results. It's a crucial step in preparing data for many machine learning tasks.