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The pancreas and surrounding vascular landmarks should be examined from the level of what?

The dose to air is found to be 3.3 Gy. What is the exposure?

What is radiographic film useful for? 

When is kerma at its maximum?

Consider the one-dimensional (i.e., = 1) training dataset in the figure below (left side, red dots represent data pairs ).   Building on the previous tree, find the regression tree with three leaves having the smallest training MSE. What is the threshold value

In this problem, we have sketched up the code for the K-Means Clustering algorithm. Please choose options to fill in the blanks. import numpy as np import matplotlib.pyplot as plt def kmeans(X,K,iteration):     N = len(X) # Number of data points     labels = np.zeros((N,1)) # Cluster labels for each data point     centroids = np.zeros((K,X.shape[1])) # Centroid of each cluster     # Innitialize: Randomly assign a number C(i) in (1,…,K) to each index i = 1…N     for i in range(len(labels)):         labels[i] = np.random.randint(0,K)             for iteration in range(iteration):          # Compute the centroid of cluster K         for k in range(K):             dp = X[np.where(labels == k)[0]]             centroids[k] = _________(1)___________                     # Assign observation n to the cluster with closest centroid         for n in range(N):             distance = np.linalg.norm(X[n]-centroids,axis=1)             labels[n] = _________(2)___________                 # Compute the distance between each data point and their centroids     within_cluster_distance = 0     for m in range(N):         within_cluster_distance += _________(3)___________             return within_cluster_distance     k_list = [] for i in range(1,10):     k_list.append(kmeans(X1,i,10))     x = np.arange(1,10) plt.plot(x,k_list) plt.xlabel(‘K’) plt.ylabel(‘Within Cluster Distance’) plt.show() The format of input $$X$$ is shown below: What should go in the second blank(2)?

Consider a dataset with points and two classes (red and blue) indicated in the figure below. (Note that (0, 0) and (1, 1) are blue points whereas (1, 0) and (0, 1) are red points).   Which one of the following equations represents a separating hyperplane for the lifted three-dimensional dataset?

Paracetamol can be used to treat the following:                                                                                                              1.            Deep vein thrombosis 2.            Asthma3.            Nausea 4.            Cough5.            Pain

Section 5. Programming Question on Decision Trees (Questions 17-22) Suppose you are asked to predict if a student will be accepted or rejected from the University of Data Science. For this task, you will be working with the Students dataset, containing a binary outcome ‘Accepted’ of 19 students. There are 5 predictors including ‘GPA’, ‘SAT Math Score’, ‘SAT Reading Score’, ‘Sex’, and ‘State’. You may assume that the following import statements have already been included: import pandas as pdimport numpy as npimport matplotlib.pyplot as pltimport graphvizimport seaborn as snsfrom IPython.display import Imageimport pydotplusfrom sklearn import tree The first thing we need to do is read in the dataset with the following code: students = pd.read_csv(‘Students.csv’) students.head()  What output would you expect to see?

The following is classified as a β-lactam antimicrobial agent:                                                                                      1.           Glycopeptides2.           Lincosamides3.           Macrolides4.           Aminoglycosides5.           Cephalosporins