In machine learning, what problem does the curse of dimensionality refer to?a)Overfitting due to excessive features or dimensionsb)Inability to handle missing datac)High computational complexity of algorithmsd)Difficulty in handling large datasets
Question
In machine learning, what problem does the curse of dimensionality refer to?a)Overfitting due to excessive features or dimensionsb)Inability to handle missing datac)High computational complexity of algorithmsd)Difficulty in handling large datasets
Solution
The curse of dimensionality in machine learning refers to a) Overfitting due to excessive features or dimensions.
When the dimensionality increases, the volume of the space increases so fast that the available data become sparse. This sparsity is problematic for any method that requires statistical significance. In order to obtain a statistically sound and reliable result, the amount of data needed to support the result often grows exponentially with the dimensionality. Also, organizing and searching data often relies on detecting areas where objects form groups with similar properties; in high dimensional data, however, all objects appear to be sparse and dissimilar in many ways, which prevents common data organization strategies from being efficient.
Similar Questions
The curse of dimensionality refers to:Question 8Answera.The complexity of high-dimensional datab.The simplicity of low-dimensional datac.The accuracy of high-dimensional datad.The process of dimensionality reduction
How is the curse of dimensionality related to the quality of fit in modeling? It improves the quality of fit It has no impact on the quality of fit It degrades the quality of fit It makes the quality of fit unpredictable
I am giving you the questions just give me the answers from the above options Which concept refers to the problem of increasing computational complexity as the number of features or dimensions in a dataset grows? Model selection The curse of dimensionality Bias-variance trade-off Overfitting
The Hughes phenomenon claims that for a fixed size dataset, a machine learning model performs worse as dimensionality risesTRUEFALSE
Which dimensionality reduction technique is affected by the curse of dimensionality?Review LaterPrincipal Component Analysis (PCA)UMAPt-SNENone of the above
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