fieldnormalizációja

Field normalization is a technique used in data preprocessing, particularly in the context of machine learning and data analysis. It involves adjusting the values of numeric columns in the data to a common scale, without distorting differences in the ranges of values. This process is crucial for algorithms that are sensitive to the magnitude of the data, such as gradient descent-based methods, k-nearest neighbors, and principal component analysis.

There are several methods for field normalization, each with its own advantages and use cases. The most common methods include:

1. Min-Max Normalization: This technique scales the data to a fixed range, usually 0 to 1. It is calculated as (x - min) / (max - min), where x is the original value, min is the minimum value in the column, and max is the maximum value in the column.

2. Z-Score Normalization: Also known as standardization, this method scales the data to have a mean of 0 and a standard deviation of 1. It is calculated as (x - mean) / std, where x is the original value, mean is the mean of the column, and std is the standard deviation of the column.

3. Decimal Scaling: This method moves the decimal point of values to scale them down. It is calculated as x / (10^j), where j is the smallest number needed to get every value in the range -1 to 1.

Field normalization is essential for ensuring that each feature contributes equally to the final analysis or model. It helps to prevent features with larger scales from dominating the learning process, which can lead to biased results. Additionally, it can improve the convergence speed of iterative algorithms and enhance the performance of distance-based algorithms. However, it is important to note that normalization should be applied to the training data and then the same transformation should be applied to the test data to avoid data leakage.