- Dimensionality reduction is a technique in machine learning and data science that is used to reduce the number of features or variables in a dataset, while retaining as much relevant information as possible. The goal of dimensionality reduction is to simplify the dataset and make it easier to analyze, visualize, and model.
- There are two main types of dimensionality reduction techniques: feature selection and feature extraction.
- Feature selection is a technique that selects a subset of the original features based on some criteria, such as their relevance to the target variable, their correlation with other features, or their predictive power. Feature selection techniques can be either supervised or unsupervised, depending on whether they use information about the target variable or not.
- Feature extraction is a technique that transforms the original features into a new set of features that capture the most relevant information in the dataset, while discarding irrelevant or redundant information. Feature extraction techniques are typically unsupervised and do not require information about the target variable.
- Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two commonly used feature extraction techniques in machine learning. PCA is a technique that reduces the dimensionality of a dataset by projecting it onto a lower-dimensional space that captures the maximum variance in the data. LDA is a technique that reduces the dimensionality of a dataset while preserving the discriminative information between classes.
- Other popular feature extraction techniques include t-SNE (t-distributed Stochastic Neighbor Embedding), UMAP (Uniform Manifold Approximation and Projection), and autoencoders, which are neural network models that learn to compress and reconstruct the data.
- Dimensionality reduction can help improve the performance of machine learning models by reducing overfitting, increasing the model’s interpretability, and reducing the time and resources required for training and inference. However, dimensionality reduction can also result in loss of information and reduction in predictive accuracy, especially if the reduced dataset does not capture all the relevant information in the original dataset.