Nedtransformasjon

Nedtransformasjon refers to a process of reducing the dimensionality of data while preserving important characteristics. This is often necessary when dealing with datasets that have a very large number of features, also known as high dimensionality. High-dimensional data can be computationally expensive to process and can lead to the "curse of dimensionality," where models perform poorly due to sparsity.

The goal of nedtransformasjon is to represent the original data in a lower-dimensional space. This can simplify data analysis, improve model performance, and reduce storage requirements. There are various techniques available for nedtransformasjon, broadly categorized into feature selection and feature extraction methods.

Feature selection involves identifying and keeping only the most relevant features from the original dataset and discarding the rest. This can be done based on statistical measures, correlation with the target variable, or using model-based approaches. Feature extraction, on the other hand, creates new features that are combinations of the original ones. These new features are designed to capture the most significant variance or information in the data.

Popular feature extraction techniques include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). PCA is a linear method that finds a set of orthogonal axes (principal components) that capture the maximum variance in the data. t-SNE is a non-linear technique primarily used for visualizing high-dimensional data in two or three dimensions, focusing on preserving local neighborhood structures. The choice of nedtransformasjon technique depends on the specific dataset and the intended application.