Lesson 4 Not-so-naïve Bayes   When you develop a not-so-naïve Bayes model using KDE with a Gaussian kernel, which assumption of the naïve Bayes algorithm is rejected?

Lesson 3 Python API’s   Which of the following is generally easiest to use for extracting online data?

Lesson 1 k-Nearest Neighbors   Let assume that a dataset consists of 4 data points. The followings are the coordinates and the label of each data point:   Point A: (2, 4) / class 0 Point B: (0, 0) / class 1 Point C: (-1, 3) / class 0 Point D: (3, 2) / class 1   If you train a 1-NN model on this dataset, what would be the prediction for Point B?

Lesson 2 Expectation-Maximization Models   Which of the following is a common feature of k-means and Gaussian mixture model?

Lesson 1 Blessing and Curse of Dimensionality   Consider two points a = 0 and b = 1 that exist in spaces with different dimensions d ∈ [2, 8, 32, 128] (e.g. if d = 2 then a = [0, 0]). The blessing of dimensionality refers to the fact that distance between points a and b:

Lesson 2 Principal Component Analysis   Which of the following is not true about PCA?

Lesson 4 Outliers   Which of the following is not true about the z-score approach?

Lesson 1 Assessing Performance of Dimensional Reduction   Which of the following is not an expectation of reducing dimensions?

Lesson 2 Visualizing Single Features   Which of the following statistics cannot be represented as a vector for a high-dimensional dataset?

Lesson 1 Simple Linear Regression   Which of the following is not true?