School of Computing and Information Systems The University of Melbourne
COMP90049 Introduction to Machine Learning (Semester 1, 2022) Week 8
1. What is the difference between “model bias” and “model variance”?
(i). Why is a high bias, low variance classifier undesirable?
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(ii). Why is a low bias, high variance classifier (usually) undesirable?
2. Describe how holdout, and cross-validation can help reduce overfitting?
3. Why ensembling reduces model variance?
4. Consider the following training set:
With the bias value of 1, the initial weight function of 𝜃 = {𝜃#,𝜃$,𝜃%} = {0.2, -0.4, 0.1} and learning rate of 𝜂 = 0.2.
Consider the activation function of the perceptron as the step function 𝑓 = . 1 𝑖𝑓 Σ > 0 0 𝑜𝑡h𝑒𝑟𝑤𝑖𝑠𝑒
a) Can the perceptron learn a perfect solution for this data set?
b) Draw the perceptron graph and calculate the accuracy of the perceptron on the training data before training?
c) Using the perceptron learning rule and the learning rate of 𝜂 = 0.2. Train the perceptron for one epoch. What are the weights after the training?
d) What is the accuracy of the perceptron on the training data after training for one epoch? Did the accuracy improve?
5. [OPTIONAL]Whyisaperceptron(whichusesasigmoidactivationfunction)equivalenttologistic regression?
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