Skemanormalisering

Skemanormalisering, also known as scale normalization, is a pre-processing technique used in data analysis and machine learning to adjust the scale of features within a dataset. The primary goal of skemanormalisering is to ensure that all features contribute equally to a model's training process, preventing features with larger ranges from dominating the learning algorithm.

This method involves transforming each feature so that it conforms to a common scale, typically by rescaling the data to a range between 0 and 1 or by converting features to have a mean of zero and a standard deviation of one. Common techniques include min-max normalization and z-score normalization. Min-max normalization rescales features to lie within a specified range, often [0, 1], by subtracting the minimum value and dividing by the range of the feature. Z-score normalization standardizes features by subtracting the mean and dividing by the standard deviation, resulting in a distribution with a mean of zero and a variance of one.

Skemanormalisering is particularly useful in algorithms that rely on distance calculations, such as k-nearest neighbors (k-NN) and support vector machines (SVM), as it ensures that no single feature unduly influences the model. It is also beneficial for gradient-based optimization methods, improving convergence speed and model performance.

While skemanormalisering enhances model stability and interpretability, it is essential to apply the normalization techniques consistently across training and testing datasets to prevent data leakage and ensure valid performance evaluation. Proper normalization contributes to obtaining more reliable and accurate machine learning models.