71.
Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of it's hyper parameter.What would happen when you use very small C (C~0)?

72.
Which of the following is a reasonable way to select the number of principal components "k"?

73.
If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class . . . . . . . .

77.
Which statement is true about neural network and linear regression models?

79.
Which of the following can act as possible termination conditions in K-Means?
1. For a fixed number of iterations.
2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum.
3. Centroids do not change between successive iterations.
4. Terminate when RSS falls below a threshold.

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