👉 Feat computing is a method used in machine learning and data science to identify and extract meaningful features from complex datasets, reducing their dimensionality while preserving important information. It involves selecting a subset of the most relevant features that capture the essential patterns and relationships within the data, making models more efficient and interpretable. This process is crucial in scenarios where datasets are high-dimensional, such as image or text data, as it helps prevent overfitting and improves the model's performance by focusing on the most informative aspects of the data. Techniques like Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and autoencoders are commonly employed in feat computing to achieve these goals.
