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Q. |
## The "curse of dimensionality" referes |

A. | all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions. |

B. | all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions. |

C. | all the problems that arise when working with data in the lower dimensions, that did not exist in the lower dimensions. |

D. | all the problems that arise when working with data in the higher dimensions, that did not exist in the higher dimensions. |

Answer» A. all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions. |

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- What does dimensionality reduction reduce?
- Dimensionality Reduction Algorithms are one of the possible ways to reduce the computation time required to build a model
- Dimensionality reduction algorithms are one of the possible ways to reduce the computation time required to build a model.
- It is not necessary to have a target variable for applying dimensionality reduction algorithms
- The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other