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Q. |
## PCA can be used for projecting and visualizing data in lower dimensions. |

A. | true |

B. | false |

Answer» A. true |

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- 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
- PCA works better if there is 1. A linear structure in the data 2. If the data lies on a curved surface and not on a flat surface 3. If variables are scaled in the same unit
- performs a PCA with non-linearly separable data sets.
- ________performs a PCA with non-linearly separable data sets.
- What is PCA, KPCA and ICA used for?
- In PCA the number of input dimensiona are equal to principal components
- PCA is
- What would you do in PCA to get the same projection as SVD?
- In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data.
- Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node