Machine Learning Interview Questions and Answers Set 7

61. What is dimensionality reduction? Explain in detail.

The process of reducing variables in an ML classification scenario is called Dimensionality reduction. The process is segregated into sub-processes called feature extraction and feature selection. Dimensionality reduction is done to enhance visualization of training data. It finds the appropriate set of variables known as principal variables.

62. Explain Principal Component Analysis (PCA).

PCA is a dimensionality-reduction technique which mathematically transforms a set of correlated variables into a smaller set of uncorrelated variables called principal components.

63. What value do you optimize when using a support vector machine (SVM)?

For a linear function, SVM optimizes the product of input vectors as well as the coefficients. In other words, the algorithm with the linear function can be restructured into a dot-product.

64. On what basis do you choose a classifier?

Classifiers must be chosen based on the accuracy it provides on the trained data. Also, the size of the dataset sometimes affects accuracy. For example, Naive Bayes classifiers suit smaller datasets in terms of accuracy due to higher asymptotic errors.

65. Mention key business metrics that help ML?

Identify the key services/products/functions that hold good for ML. For example, if you consider a commercial bank, metrics such as a number of new accounts, type of accounts, leads generated and so on, can be evaluated through ML methods.

66. What is kernel SVM?

Kernel SVM is the abbreviated version of kernel support vector machine. Kernel methods are a class of algorithms for pattern analysis and the most common one is the kernel SVM.

67. What is the decision tree classification? 

A decision tree builds classification (or regression) models as a tree structure, with datasets broken up into ever smaller subsets while developing the decision tree, literally in a tree-like way with branches and nodes. Decision trees can handle both categorical and numerical data.

68. What is a recommendation system? 

Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system: It’s an information filtering system that predicts what a user might want to hear or see based on choice patterns provided by the user.

69. What are the five popular algorithms of Machine Learning?

  1. Decision Trees
  2. Neural Networks (back propagation)
  3. Probabilistic networks
  4. Nearest Neighbor
  5. Support vector machines

70. What are the different Algorithm techniques in Machine Learning?

The different types of techniques in Machine Learning are

  1. Supervised Learning
  2. Unsupervised Learning
  3. Semi-supervised Learning
  4. Reinforcement Learning
  5. Transduction
  6. Learning to Learn