41. What would you do in PCA to get the same projection as SVD?
42. The . . . . . . . . of the hyperplane depends upon the number of features.
43. What is the approach of basic algorithm for decision tree induction?
44. Can we extract knowledge without apply feature selection
45. 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
46. When the number of classes is large Gini index is not a good choice.
47. Data used to build a data mining model.
48. This technique associates a conditional probability value with each data instance.
49. What is the purpose of the Kernel Trick?
50. Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.
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