Curse Of Dimensionality
Curse of Dimensionality refers to the phenomenon where the performance of certain algorithms in machine learning and data mining deteriorates as the number of dimensions in the data increases. As the number of dimensions increases, the volume of the data increases exponentially, making it difficult for algorithms to identify patterns and relationships in the data. This results in overfitting, where the model fits the training data too closely and fails to generalize to new data. The curse of dimensionality can be mitigated by feature selection, dimensionality reduction techniques such as Principal Component Analysis (PCA), and using algorithms that are specifically designed to handle high-dimensional data such as Random Forest and Gradient Boosting.
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