31. This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.
32. Following is powerful distance metrics used by Geometric model
33. If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for?
34. How does number of observations influence overfitting? Choose the correct answer(s).Note: Rest all parameters are same
1. In case of fewer observations, it is easy to overfit the data.
2. In case of fewer observations, it is hard to overfit the data.
3. In case of more observations, it is easy to overfit the data.
4. In case of more observations, it is hard to overfit the data.
1. In case of fewer observations, it is easy to overfit the data.
2. In case of fewer observations, it is hard to overfit the data.
3. In case of more observations, it is easy to overfit the data.
4. In case of more observations, it is hard to overfit the data.
35. The . . . . . . . . parameter can assume different values which determine how the data matrix is initially processed.
36. Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting. Which of the following is best option would you more likely to consider iterating SVM next time?
37. How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?
38. A regression model in which more than one independent variable is used to predict the dependent variable is called
39. If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance?
40. SVM algorithms use a set of mathematical functions that are defined as the kernel.
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