[SOLVED] 代写代考 COMP9417-Machine Learning & Data Mini…

COMP9417-Machine Learning & Data Mini…
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Which of the following models can NOT achieve training error of zero on any linearly separable dataset? (you can choose more than one option)
Select one or more:
a. Perceptron
b. Decision tree
c. Hard-margin SVM
Your answer is incorrect.
The correct answer is: 15-NN
On the dataset below with two features x1, x2, which of the following decision boundaries could be learned by 1-NN (K Nearest Neighbour with K=1)? (select only one option)
Select one: a.
Your answer is incorrect. The correct answer is:
A random variable follows an exponential distribution with parameter ¦Ë (¦Ë > 0) if it has the following density:
This distribution is often used to model waiting times between events. Imagine you are given i.i.d. data X = (x1,…,xm) where each xi is modelled as being drawn from an exponential distribution with parameter ¦Ë
Question: what is the log-probability of X given ¦Ë? (log-probability = )
Select one: a.
Your answer is incorrect. The correct answer is:
Suppose that you are given the below dataset of mushroom types with two features of ¡°color¡± and whether the mushroom ¡°is spotted¡± and the output variable of whether the mushroom ¡°is poisonous¡±. Which of the following statements is/are correct assuming that features are conditionally independent?
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x1 = Color
dark brown white dark brown white dark brown light brown white
x2 = Is Spotted
yes yes no no no no yes
y = Is Poisonous Yes
Select one or more:
a. P(y = Yes) = `3/7`
b. P(x1 = white, x2 = yes|y = Yes) = `2/9`
c. P(x1 = white, x2 = yes|y = Yes) = `1/3`
d. P(x1 = white, x2 = yes|y = Yes) = `2/7`
Your answer is incorrect. The correct answers are:
P(y = Yes) = `3/7`,
P(x1 = white, x2 = yes|y = Yes) = `2/9`
Which of the following statements is/are CORRECT about ensemble learning? (you can choose more than one option)
Select one or more:
a. Bagging is useful to reduce the bias if algorithms are high bias
b. In Bagging, bootstrap is used to sample from features
c. In Bagging, models are trained in parallel
d. Bagging is useful to reduce the variance with big data
Your answer is incorrect.
The correct answer is:
In Bagging, models are trained in parallel
Supposed that you have applied Decision Tree algorithm to your training set and validation set and you have got the following scenarios by choosing different depth for your algorithm. Which depth you will pick for your final model?
Select one: a. 4
b. 6 c. 3 d. 5
Your answer is incorrect.
The correct answer is: 4
Training error
30% 26% 22% 15%
Validation error
35% 30% 30% 35%
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