本次美国代写是一个Python人工智能概念相关的编程作业

**1. Perceptron versus Naive Bayes classifiers**

Compare the perceptron and naive Bayes. How are they different and how are they alike? provide at least four

comparison statements.

**2. Perceptron**

Consider the two dimensional examples (0, 0), (0, 1), (1, 0), (1, 1) that are all positive examples, and the

examples, (2.1, 0), (0, 2.5), (1.6, 1.6) that are all negative examples.

(a) Are these examples linearly separable? Show your work.

(b) Find a perceptron that separates the examples. Use the step function that outputs 0 for negative examples

and 1 otherwise.

**3. K-means Clustering**

(a) In the K-means lecture, we initialized the center clusters µ1; · · · µk randomly. Discuss how effective it would

be to initializing all the center clusters µ1; · · · µk to a vector of zeros.

(b) Should the cost function J seen in lecture , decrease, increase or stay the same after each iteration of

K-means?

(c) We know that K-means is sensitive to the initialization of the centers. The figures below show a dataset

with three clusters. Each figure has a different initialization of the four centers (the black points are the

centers). Which pair of initialization will lead to the same clustering? Mark all that apply.

**4. Interpretability**

You have a received a training data set and the best performing method to predict the label of interest is a

random forests.

You need to hand your classifier and discuss with collaborators the reason of the classification. Showing one

thousand trees is not satisfactory (even if each individual tree is an explanation by itself).

Propose an algorithm to extract explanations from a random forest model. Please be specific and clear about

the type of representation and interpretability method you propose. Feel free to research the topic and cite all

your sources.

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