The curse of dimensionality refers to the problems that arise when analyzing data with many variables (dimensions). As the number of variables increases, the volume of the space increases so quickly that data points become sparse, making it harder to detect meaningful patterns or relationships.
To overcome the curse of dimensionality, analysts often use techniques such as dimensionality reduction (e.g., principal component analysis or PCA), feature selection, regularization, or clustering to reduce the number of variables or summarize the data into meaningful groups. These methods help improve model performance and generalizability in high-dimensional settings.
For example, in a machine learning project, adding too many predictors can cause a model to overfit the training data but perform poorly on new data, because the model struggles to generalize in high-dimensional space. To address this, the team might apply principal component analysis to reduce the number of input variables and improve model stability.
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