Data Scrubbing Operation: Dimension Reduction
Explore how dimension reduction techniques simplify complex datasets by reducing variables while preserving key information. Understand how this mathematical transformation improves computational efficiency, enables better pattern recognition, and aids visualization in machine learning applications. Gain practical insights into applying these methods during data scrubbing to enhance model performance and interpretability.
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Quick overview
Dimension reduction, also known as descending dimension algorithms, transforms data to a lower dimension. This can help to lessen computational resources and visualize patterns in the data.
Dimensions are the number of variables describing the data, such as the city of residence, country of residence, age, and gender. Four variables can be plotted on a scatterplot, but three-dimensional and two-dimensional plots are easiest for the human eye to interpret.
The goal of a descending ...