51. What is the actual number of independent parameters which need to be estimated in P dimensional Gaussian distribution model?
52. Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting
53. Which Association Rule would you prefer
54. Supervised learning differs from unsupervised clustering in that supervised learning requires
55. Type of matrix decomposition model is
56. Bayes Theorem is given by where
1. P(H) is the probability of hypothesis H being true.
2. P(E) is the probability of the evidence(regardless of the hypothesis).
3. P(E|H) is the probability of the evidence given that hypothesis is true.
4. P(H|E) is the probability of the hypothesis given that the evidence is there.
1. P(H) is the probability of hypothesis H being true.
2. P(E) is the probability of the evidence(regardless of the hypothesis).
3. P(E|H) is the probability of the evidence given that hypothesis is true.
4. P(H|E) is the probability of the hypothesis given that the evidence is there.
57. We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data?
58. Which of the following is true about Residuals ?
59. Prediction is
60. Function used for linear regression in R is
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