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Q. |
## SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = 1jx) |

A. | True |

B. | false |

Answer» B. false |

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- SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)
- Let S1 and S2 be the set of support vectors and w1 and w2 be the learnt weight vectors for a linearly separable problem using hard and soft margin linear SVMs respectively. Which of the following are correct?
- The SVMs are less effective when
- Linear SVMs have no hyperparameters that need to be set by cross-validation
- Linear SVMs have no hyperparameters
- Linear SVMs have no hyperparameters that need to be set by cross-validation
- Linear SVMs have no hyperparameters that need to be set by cross-valid
- Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other
- Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model?
- which of the following cases will K-Means clustering give poor results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes