程序代写案例-CS4/CSU44061

TRINITY COLLEGE DUBLIN
School of Computer Science and Statistics
Week 2 Assignment CS7CS4/CSU44061 Machine Learning
Rules of the game:
• Its ok
to discuss with others, but do not show any code you write to others. You must
write answers in your own words and write code entirely yourself. All submissions will
be checked for plagiarism.
• Reports must be typed (no handwritten answers please) and submitted as a separate
pdf on Blackboard (not as part of a zip file please).
• Important: For each problem, your primary aim is to articulate that you understand
what you’re doing - not just running a program and quoting numbers it outputs. Long
rambling answers and “brain dumps” are not the way to achieve this. If you write code
to carry out a calculation you need to discuss/explain what that code does, and if you
present numerical results you need to discuss their interpretation. Generally most of the
credit is given for the explanation/analysis as opposed to the code/numerical answer.
Saying “see code” is not good enough, even if code contains comments. Similarly,
standalone numbers or plots without further comment is not good enough.
• When your answer includes a plot be sure to (i) label the axes, (ii) make sure all the
text (including axes labels/ticks) is large enough to be clearly legible and (iii) explain
in text what the plot shows.
• Include the source of code written for the assignment as an appendix in your submitted
pdf report. Also include a separate zip file containing the executable code and any data
files needed. Programs should be running code written in Python, and should load data
etc when run so that we can unzip your submission and just directly run it to check
that it works. Keep code brief and clean with meaningful variable names etc.
• Reports should typically be about 5 pages, with 10 pages the upper limit (excluding
appendix with code). If you go over 10 pages then the extra pages will not be marked.
Downloading Dataset
• Download the assignment dataset from https://www.scss.tcd.ie/Doug.Leith/CSU44061/
week2.php. Important: You must fetch your own copy of the dataset, do not use the
dataset downloaded by someone else.
• Please cut and paste the first line of the data file (which begins with a #) and include
in your submission as it identifies your dataset.
• The data file consists of three columns of data (plus the first header line). The first
two columns are input features and the third column is the ±1 valued target value.
.
Assignment
Use sklearn for this assignment, there’s no need to implement any of the models used here
yourself. Include the code you write in an appendix to your submission. To read in the
data file you can, for example, use pandas:
import numpy as np
import pandas as pd
df = pd . r ead c sv (” week2 . csv ”)
p r i n t ( df . head ( ) )
X1=df . i l o c [ : , 0 ]
X2=df . i l o c [ : , 1 ]
X=np . column stack ( (X1 , X2) )
y=df . i l o c [ : , 2 ]
(a) (i) Visualise the data you downloaded by placing a marker on a 2D plot for each
pair of feature values i.e. for each row in the data. On the plot the x-axis should
be the value of the first feature, the y-axis the value of the second feature and
the marker should be, for example, a + marker when the target value is +1 and
a o when the target is −1. Your plot should look similar in style to this (with
different data points of course!):
−1.0 −0.5 0.0 0.5 1.0
x_1
−1.0
−0.5
0.0
0.5
1.0
x
_
2
Be sure to include a legend explaining what markers/colours are used for the +1
and -1 points.
(ii) Use sklearn to train a logistic regression classifier on the data. Give the logistic
regression model for predictions and report the parameter values of the trained
model. Discuss e.g. which feature has most infliuence on the prediction, which
features cause the prediction to increase and which to decrease.
(iii) Now use the trained logistic regression classifier to predict the target values
in the training data. Add these predictions to the 2D plot you generated in
part (i), using a different marker and colour so that the training data and the
predictions can be distinguished. Show the decision boundary of the logistic
regression classifier as a line on the plot (you’ll need to use the parameter values
of the trained model to figure out what line this should be - explain how you
obtain it).
(iv) Briefly comment on how the predictions and the training data compare.
(b) Use sklearn to train linear SVM classifiers on your data (nb: be sure to use the
LinearSVC function in sklearn, not the SVC function).
(i) Train linear SVM classifiers for a wide range of values of the penalty parameter
C e.g. C = 0.001, C = 1, C = 100. Give the SVM model for predictions and
report the parameter values of each trained model.
(ii) Use each of these trained classifiers to predict the target values in the training
data. Plot these predictions and the actual target values from the data, together
with the classifier decision boundary.
(iii) What is the impact on the model parameters of changing C, and why ? What
is the impact on the SVM predictions?
(iv) How do the SVM model parameters and predictions compare to those of the
logistic regression model in part (a)?
(c) (i) Now create two additional features by adding the square of each feature (i.e.
giving four features in total). Train a logistic regression classifier. Give the
model and the trained parameter values.
(ii) Use the trained classifier to predict the target values in the training data. Plot
these predictions and the actual target values from the data using the same
style of plot as before i.e. using just the two original features as x and y axes.
Compare and discuss. How do the predictions compare with those in parts (a)
and (b) above?
(iii) Compare the performance of the classifier against a reasonable baseline predictor,
e.g. one that always predicts the most common class.
2
(iv) For a bonus try to plot the classifier decision boundary (nb: it can be derived
from the model parameters but its not a straight line anymore and you’ll need
to solve a quadratic equation). Don’t worry if you can’t do this though.
3

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