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Consider kNN, linear regression (LR), Decision tree (DT) learning (using correlation for splitting), and Random tree. Which model has the slowest in query time?

What is the output of the following Python code snippet? >>> import numpy as np >>> np.random.seed(5) >>> x = np.random.uniform(0, 4) >>> np.random.seed(5) >>> y = np.random.uniform(0, 4) >>> z = np.random.uniform(0, 4) >>> print(z == y, x == z)

What is the output of the following Python session? >>> def func(k = 4): … j = 8 … return k + j … >>> q = 7 >>> j = 3 >>> print(func())

Consider the following order book. Bid size Price Ask size 200.80 300 200.75 200 200.50 100 200.25 100 100 199.95 400 199.90 100 199.85 At what average price would a limit order to BUY 200 shares at $200.75 be executed?

What is the output of the following Python session? >>> x = 5 >>> y = x >>> x = 3 >>> print(y * x)

Which of the following is NOT an advantage of ensemble learners?

Consider the following order book. Bid size Price Ask size 55.90 400 55.85 300 55.75 300 100 55.45 75 55.35 200 55.25 At what average price would a market order to BUY 100 shares be executed?

Plotting in sample and out of sample model error against model complexity can be used to visualize overfitting. Overfitting in this graph is when:

Which of the following are generally true of instance-based models?

Which is an advantage of using an ensemble learner?