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python代写-CITS5508

时间：2021-03-24

CITS5508 Machine Learning

Question 1 (10 marks)

Suppose that you are given a complex, multiclass classification problem and that your machine learning

library only has the Support Vector classifier. Describe all the steps that you would take to train this

classifier for your problem.

Question 2 (10 marks)

In binary classification, precision and recall are computed as follows:

Precision =

TP

TP + FP

Recall =

TP

TP + FN

where TP, FP, and FN denote, respectively, the numbers of true positives, false positives, and false

negatives. Given below is the confusion matrix from a classifier on a 3-class classification problem:

1

The classes are three common types of domestic pets: dog, cat, and fish.

(i) Describe how the average precision and average recall can be computed from the matrix. Include

(8 marks)appropriate diagrams in your description.

(ii) What are the average precision and average recall of this classifier? (2 marks)

Note: we want the straight average (not the weighted average) precision and recall. You can include

some Python code or just use a calculator to show your calculation. Your results only need to be accurate

up to the first decimal place.

Question 3 (10 marks)

Given in the webpage below:

https://archive.ics.uci.edu/ml/datasets/Auto+MPG

is a dataset auto-mpg.data for regression. The dataset contains 398 instances and 9 columns. Our

objective is to predict the fuel efficiency, i.e., the numbers of miles travelled per gallon of fuel (miles per

gallon, abbreviated as mpg in the spreadsheet), using (all or some of) the remaining columns. Describe

the data cleaning and data preparation process you would carry out before a suitable machine learning

regressor can be applied. Your data cleaning and data preparation process should be specific to the dataset

itself. You can write some Python code to read1 and inspect the dataset and visualise the features but the

code should not be included in your answer. To keep your answer to one page long, visualisation should

be limited to one single figure (which may contain subplots).

Question 4 (10 marks)

(i) Ridge regression, Lasso regression, and Elastic Net are regularisation functions that can be added

to your cost function to help overcome the overfitting problem. They all involve one or two regu-

larisation coefficients (referred to as α and r in the textbook), which are hyperparameters that need

to be optimally determined. Comment on the problems when these coefficients are too small or too

large. (3 marks)

(ii) Suppose that you have implemented two Support Vector classifiers using the polynomial kernel

and radial basis function (RBF) kernel respectively. Suppose that you set the hyperparameter

r (corresponding to the coef0 in the SVC class in the Scikit-learn library) to 0. So the kernels

effectively become:

Polynomial kernel of degree d: K(a,b) = (γa>b)d

Gaussian RBF: K(a,b) = exp

(−γ‖a− b‖2)

If you experience an overfitting issue in both classifiers, how would you adjust the hyperparameters

d and γ? Explain your answer. (4 marks)

(iii) Suppose that you need to develop a machine learning algorithm for a fire alarm system to alert

people on fire incidents. For the safety of the people living in the area, you want your fire alarm

system to be extremely sensitive so that it won’t miss any true fire incidents. Comment on

1Use the read csv function of pandas with sep=’\s+’

2

(1 mark)

(1 mark)

a) the trade-off between the precision and recall values for your ML algorithm,

b) how you would like the confusion matrix to look like, and

c) the decision threshold that you would set in your algorithm. (1 mark)

Hint: See Figures 3-3 and 3-4 in the textbook.

Question 5 (10 marks)

(i) Give an example for each of the following:

a) binary classification, (1 mark)

b) multiclass classification, (1 mark)

c) multilabel classification, and (1 mark)

d) multioutput multiclass classification. (1 mark)

Your examples MUST NOT be the same as any examples that have been mentioned in the lecture

notes or in the textbook. Where relevant, you should state the number of classes and number of

labels in your examples.

(ii) Go through each line of the code snippet below and explain what it does and what the code tries to

achieve. (6 marks)

from sklearn.linear_model import SGDRegressor

from sklearn.metrics import mean_squared_error

import matplotlib.pyplot as plt

sgd = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True,

penalty=None, learning_rate=’constant’, eta0=0.005)

min_val_err = np.float("inf")

best_epoch = None

best_sgd = None

for epoch in range(500):

sgd.fit(X_train, y_train)

y_pred = sgd.predict(X_val)

err = mean_squared_error(y_val, y_pred)

if err < min_val_err:

min_val_err = err

best_epoch = epoch

best_sgd = sgd

print(’Best epoch =’, best_epoch)

y_pred = best_sgd.predict(X_val)

print(’Mean squared error of the best model is’,

mean_squared_error(y_val, y_pred))

plt.plot(X_val, y_val, ’ro’, X_val, y_pred, ’bx’)

plt.legend([’ground truth’, ’prediction’])

plt.show()

You may assume that

3

• the data has been appropriately split into the training set (X train and y train) and validation

set (X val and y val);

• both X train and X val have feature dimension equal to 1.

Hint: You will need to look up the Scikit-learn library for the various functions in the code.

学霸联盟

Question 1 (10 marks)

Suppose that you are given a complex, multiclass classification problem and that your machine learning

library only has the Support Vector classifier. Describe all the steps that you would take to train this

classifier for your problem.

Question 2 (10 marks)

In binary classification, precision and recall are computed as follows:

Precision =

TP

TP + FP

Recall =

TP

TP + FN

where TP, FP, and FN denote, respectively, the numbers of true positives, false positives, and false

negatives. Given below is the confusion matrix from a classifier on a 3-class classification problem:

1

The classes are three common types of domestic pets: dog, cat, and fish.

(i) Describe how the average precision and average recall can be computed from the matrix. Include

(8 marks)appropriate diagrams in your description.

(ii) What are the average precision and average recall of this classifier? (2 marks)

Note: we want the straight average (not the weighted average) precision and recall. You can include

some Python code or just use a calculator to show your calculation. Your results only need to be accurate

up to the first decimal place.

Question 3 (10 marks)

Given in the webpage below:

https://archive.ics.uci.edu/ml/datasets/Auto+MPG

is a dataset auto-mpg.data for regression. The dataset contains 398 instances and 9 columns. Our

objective is to predict the fuel efficiency, i.e., the numbers of miles travelled per gallon of fuel (miles per

gallon, abbreviated as mpg in the spreadsheet), using (all or some of) the remaining columns. Describe

the data cleaning and data preparation process you would carry out before a suitable machine learning

regressor can be applied. Your data cleaning and data preparation process should be specific to the dataset

itself. You can write some Python code to read1 and inspect the dataset and visualise the features but the

code should not be included in your answer. To keep your answer to one page long, visualisation should

be limited to one single figure (which may contain subplots).

Question 4 (10 marks)

(i) Ridge regression, Lasso regression, and Elastic Net are regularisation functions that can be added

to your cost function to help overcome the overfitting problem. They all involve one or two regu-

larisation coefficients (referred to as α and r in the textbook), which are hyperparameters that need

to be optimally determined. Comment on the problems when these coefficients are too small or too

large. (3 marks)

(ii) Suppose that you have implemented two Support Vector classifiers using the polynomial kernel

and radial basis function (RBF) kernel respectively. Suppose that you set the hyperparameter

r (corresponding to the coef0 in the SVC class in the Scikit-learn library) to 0. So the kernels

effectively become:

Polynomial kernel of degree d: K(a,b) = (γa>b)d

Gaussian RBF: K(a,b) = exp

(−γ‖a− b‖2)

If you experience an overfitting issue in both classifiers, how would you adjust the hyperparameters

d and γ? Explain your answer. (4 marks)

(iii) Suppose that you need to develop a machine learning algorithm for a fire alarm system to alert

people on fire incidents. For the safety of the people living in the area, you want your fire alarm

system to be extremely sensitive so that it won’t miss any true fire incidents. Comment on

1Use the read csv function of pandas with sep=’\s+’

2

(1 mark)

(1 mark)

a) the trade-off between the precision and recall values for your ML algorithm,

b) how you would like the confusion matrix to look like, and

c) the decision threshold that you would set in your algorithm. (1 mark)

Hint: See Figures 3-3 and 3-4 in the textbook.

Question 5 (10 marks)

(i) Give an example for each of the following:

a) binary classification, (1 mark)

b) multiclass classification, (1 mark)

c) multilabel classification, and (1 mark)

d) multioutput multiclass classification. (1 mark)

Your examples MUST NOT be the same as any examples that have been mentioned in the lecture

notes or in the textbook. Where relevant, you should state the number of classes and number of

labels in your examples.

(ii) Go through each line of the code snippet below and explain what it does and what the code tries to

achieve. (6 marks)

from sklearn.linear_model import SGDRegressor

from sklearn.metrics import mean_squared_error

import matplotlib.pyplot as plt

sgd = SGDRegressor(max_iter=1, tol=-np.infty, warm_start=True,

penalty=None, learning_rate=’constant’, eta0=0.005)

min_val_err = np.float("inf")

best_epoch = None

best_sgd = None

for epoch in range(500):

sgd.fit(X_train, y_train)

y_pred = sgd.predict(X_val)

err = mean_squared_error(y_val, y_pred)

if err < min_val_err:

min_val_err = err

best_epoch = epoch

best_sgd = sgd

print(’Best epoch =’, best_epoch)

y_pred = best_sgd.predict(X_val)

print(’Mean squared error of the best model is’,

mean_squared_error(y_val, y_pred))

plt.plot(X_val, y_val, ’ro’, X_val, y_pred, ’bx’)

plt.legend([’ground truth’, ’prediction’])

plt.show()

You may assume that

3

• the data has been appropriately split into the training set (X train and y train) and validation

set (X val and y val);

• both X train and X val have feature dimension equal to 1.

Hint: You will need to look up the Scikit-learn library for the various functions in the code.

学霸联盟