Dimension reduction is the process of reducing the number of features (or dimensions) in a dataset while retaining as much information as possible. This is particularly useful in machine learning and data analysis for several reasons: 1. **Simplifying Models**: Reducing the number of dimensions can lead to simpler models that are easier to interpret and require less computational power. 2. **Improving Performance**: It can help improve the performance of machine learning algorithms by reducing overfitting.
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