1. The effectiveness of an SVM depends upon:
2. You are given sesimic data and you want to predict next earthquake , this is an example of
3. When the C parameter is set to infinite, which of the following holds true?
4. The firing rate of a neuron
5. To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?
6. During the last few years, many . . . . . . . . algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state.
7. Which of the following indicates the fundamental of least squares?
8. What is 'Training set'?
9. Which of the following statement is true about k-NN algorithm?
1. k-NN performs much better if all of the data have the same scale
2. k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large
3. k-NN makes no assumptions about the functional form of the problem being solved
1. k-NN performs much better if all of the data have the same scale
2. k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large
3. k-NN makes no assumptions about the functional form of the problem being solved
10. In Naive Bayes equation $$P\left( {C|X} \right) = \frac{{P\left( {X|C} \right) * P\left( C \right)}}{{P\left( X \right)}}$$ which part considers "likelihood"?
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