Dimensionality Reduction
Dimensionality reduction simplifies datasets by reducing input features, improving model performance and preventing overfitting in AI.
Dimensionality reduction is the process of reducing the number of input features in a dataset while retaining as much useful information as possible. Techniques like Principal Component Analysis (PCA) and t-SNE are used to simplify datasets, improving model efficiency and reducing the risk of overfitting, particularly when working with high-dimensional data.