Reducing the dimensionality
In this section, we will explore the concept of dimensionality reduction, a critical technique in machine learning and data analysis that aims to reduce the number of features or variables in a dataset while preserving essential information. High-dimensional datasets often suffer from the “curse of dimensionality,” leading to increased computational complexity and potential overfitting. Dimensionality reduction methods help to transform data into a lower-dimensional space, enabling easier visualization, improved model performance, and enhanced interpretability.
Here, we will delve into various dimensionality reduction techniques, and their applications, and provide code examples in Python to implement them effectively.
Principal component analysis
Principal Component Analysis (PCA) is a widely used linear dimensionality reduction technique that projects data onto orthogonal axes to capture the maximum variance in the first principal...
